From c916964f470401af9c0e7be81b3aa005a315ad9d Mon Sep 17 00:00:00 2001
From: naeemkh <khoshnevis.naeem@gmail.com>
Date: Wed, 6 Dec 2023 15:58:20 -0500
Subject: [PATCH 1/5] update site

---
 DESCRIPTION                                  | 2 +-
 docs/404.html                                | 2 +-
 docs/CODE_OF_CONDUCT.html                    | 2 +-
 docs/LICENSE-text.html                       | 2 +-
 docs/articles/CRE.html                       | 2 +-
 docs/articles/Contribution.html              | 2 +-
 docs/articles/Testing-the-Package.html       | 2 +-
 docs/articles/index.html                     | 2 +-
 docs/authors.html                            | 6 +++---
 docs/index.html                              | 2 +-
 docs/news/index.html                         | 2 +-
 docs/pkgdown.yml                             | 2 +-
 docs/reference/CRE-package.html              | 2 +-
 docs/reference/autoplot.cre.html             | 2 +-
 docs/reference/check_hyper_params.html       | 2 +-
 docs/reference/check_input_data.html         | 2 +-
 docs/reference/check_method_params.html      | 2 +-
 docs/reference/cre.html                      | 2 +-
 docs/reference/discover_rules.html           | 2 +-
 docs/reference/estimate_cate.html            | 2 +-
 docs/reference/estimate_ite.html             | 2 +-
 docs/reference/estimate_ite_aipw.html        | 2 +-
 docs/reference/estimate_ite_bart.html        | 2 +-
 docs/reference/estimate_ite_cf.html          | 2 +-
 docs/reference/estimate_ite_slearner.html    | 2 +-
 docs/reference/estimate_ite_tlearner.html    | 2 +-
 docs/reference/estimate_ite_tpoisson.html    | 2 +-
 docs/reference/estimate_ite_xlearner.html    | 2 +-
 docs/reference/estimate_ps.html              | 2 +-
 docs/reference/evaluate.html                 | 2 +-
 docs/reference/extract_effect_modifiers.html | 2 +-
 docs/reference/extract_rules.html            | 2 +-
 docs/reference/filter_correlated_rules.html  | 2 +-
 docs/reference/filter_extreme_rules.html     | 2 +-
 docs/reference/filter_irrelevant_rules.html  | 2 +-
 docs/reference/generate_cre_dataset.html     | 2 +-
 docs/reference/generate_rules.html           | 2 +-
 docs/reference/generate_rules_matrix.html    | 2 +-
 docs/reference/get_logger.html               | 2 +-
 docs/reference/honest_splitting.html         | 2 +-
 docs/reference/index.html                    | 2 +-
 docs/reference/interpret_rules.html          | 2 +-
 docs/reference/plot.cre.html                 | 2 +-
 docs/reference/predict.cre.html              | 2 +-
 docs/reference/print.cre.html                | 2 +-
 docs/reference/select_rules.html             | 2 +-
 docs/reference/set_logger.html               | 2 +-
 docs/reference/standardize_rules_matrix.html | 2 +-
 docs/reference/summary.cre.html              | 2 +-
 docs/search.json                             | 2 +-
 50 files changed, 52 insertions(+), 52 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 02370c13..928355d0 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: CRE
 Title: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
-Version: 0.2.5
+Version: 0.2.5.9000
 Authors@R: c(
     person("Naeem", "Khoshnevis", , "nkhoshnevis@g.harvard.edu", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-4315-1426", AFFILIATION = "FASRC")),
diff --git a/docs/404.html b/docs/404.html
index 2e0731bf..8667a4b7 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://github.com/NSAPH-Software/CRE/index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html
index cb95e14b..0034631d 100644
--- a/docs/CODE_OF_CONDUCT.html
+++ b/docs/CODE_OF_CONDUCT.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index b0c7879b..f82d5fd6 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/CRE.html b/docs/articles/CRE.html
index 65ce9084..9f7195fd 100644
--- a/docs/articles/CRE.html
+++ b/docs/articles/CRE.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/Contribution.html b/docs/articles/Contribution.html
index 940a1ed2..b5806066 100644
--- a/docs/articles/Contribution.html
+++ b/docs/articles/Contribution.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/Testing-the-Package.html b/docs/articles/Testing-the-Package.html
index 38d1332f..f9407ce8 100644
--- a/docs/articles/Testing-the-Package.html
+++ b/docs/articles/Testing-the-Package.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/index.html b/docs/articles/index.html
index bc309940..d02be64f 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/authors.html b/docs/authors.html
index 037492ef..51325a43 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -87,13 +87,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Khoshnevis N, Garcia D, Cadei R, Lee K, Bargagli Stoffi F (2023).
 <em>CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects</em>.
-R package version 0.2.5, <a href="https://github.com/NSAPH-Software/CRE">https://github.com/NSAPH-Software/CRE</a>. 
+R package version 0.2.5.9000, <a href="https://github.com/NSAPH-Software/CRE">https://github.com/NSAPH-Software/CRE</a>. 
 </p>
       <pre>@Manual{,
   title = {CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},
   author = {Naeem Khoshnevis and Daniela Maria Garcia and Riccardo Cadei and Kwonsang Lee and Falco Joannes {Bargagli Stoffi}},
   year = {2023},
-  note = {R package version 0.2.5},
+  note = {R package version 0.2.5.9000},
   url = {https://github.com/NSAPH-Software/CRE},
 }</pre>
     </div>
diff --git a/docs/index.html b/docs/index.html
index 7786f451..f5bcee73 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/news/index.html b/docs/news/index.html
index 31a22491..70560cb3 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 23f3ba7e..eb906830 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   CRE: CRE.html
   Contribution: Contribution.html
   Testing-the-Package: Testing-the-Package.html
-last_built: 2023-12-06T15:19Z
+last_built: 2023-12-06T20:56Z
 urls:
   reference: https://github.com/NSAPH-Software/CRE/reference
   article: https://github.com/NSAPH-Software/CRE/articles
diff --git a/docs/reference/CRE-package.html b/docs/reference/CRE-package.html
index 44d71d99..a680cbb7 100644
--- a/docs/reference/CRE-package.html
+++ b/docs/reference/CRE-package.html
@@ -46,7 +46,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/autoplot.cre.html b/docs/reference/autoplot.cre.html
index 3adc35ab..367994ed 100644
--- a/docs/reference/autoplot.cre.html
+++ b/docs/reference/autoplot.cre.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/check_hyper_params.html b/docs/reference/check_hyper_params.html
index ad0b2112..aee40836 100644
--- a/docs/reference/check_hyper_params.html
+++ b/docs/reference/check_hyper_params.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/check_input_data.html b/docs/reference/check_input_data.html
index 7430b514..dc2a9a26 100644
--- a/docs/reference/check_input_data.html
+++ b/docs/reference/check_input_data.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/check_method_params.html b/docs/reference/check_method_params.html
index 6410b2cd..a10740d4 100644
--- a/docs/reference/check_method_params.html
+++ b/docs/reference/check_method_params.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/cre.html b/docs/reference/cre.html
index cb96a7d3..07a62864 100644
--- a/docs/reference/cre.html
+++ b/docs/reference/cre.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/discover_rules.html b/docs/reference/discover_rules.html
index bc2cb8f9..a682b2c6 100644
--- a/docs/reference/discover_rules.html
+++ b/docs/reference/discover_rules.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_cate.html b/docs/reference/estimate_cate.html
index 604fc2a5..86bbd1f2 100644
--- a/docs/reference/estimate_cate.html
+++ b/docs/reference/estimate_cate.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_ite.html b/docs/reference/estimate_ite.html
index e4826304..17bfc29d 100644
--- a/docs/reference/estimate_ite.html
+++ b/docs/reference/estimate_ite.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_ite_aipw.html b/docs/reference/estimate_ite_aipw.html
index e57b9cef..f43af90d 100644
--- a/docs/reference/estimate_ite_aipw.html
+++ b/docs/reference/estimate_ite_aipw.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_ite_bart.html b/docs/reference/estimate_ite_bart.html
index df3abc4a..a9d67080 100644
--- a/docs/reference/estimate_ite_bart.html
+++ b/docs/reference/estimate_ite_bart.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_ite_cf.html b/docs/reference/estimate_ite_cf.html
index 3830e439..195b3751 100644
--- a/docs/reference/estimate_ite_cf.html
+++ b/docs/reference/estimate_ite_cf.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_ite_slearner.html b/docs/reference/estimate_ite_slearner.html
index a15ac6fe..8693eb2c 100644
--- a/docs/reference/estimate_ite_slearner.html
+++ b/docs/reference/estimate_ite_slearner.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_ite_tlearner.html b/docs/reference/estimate_ite_tlearner.html
index 5ab9046c..ac17ea0f 100644
--- a/docs/reference/estimate_ite_tlearner.html
+++ b/docs/reference/estimate_ite_tlearner.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/estimate_ite_tpoisson.html b/docs/reference/estimate_ite_tpoisson.html
index 104d1564..450c5a64 100644
--- a/docs/reference/estimate_ite_tpoisson.html
+++ b/docs/reference/estimate_ite_tpoisson.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">CRE</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.5.9000</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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Violating terms may lead temporary permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"CRE","text":"Installing CRAN. Installing latest developing version. Import.","code":"install.packages(\"CRE\") library(devtools) install_github(\"NSAPH-Software/CRE\", ref = \"develop\") library(\"CRE\")"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"arguments","dir":"Articles","previous_headings":"","what":"Arguments","title":"CRE","text":"Data (required) y observed response/outcome vector (binary continuous). z treatment/exposure/policy vector (binary). X covariate matrix (binary continuous). Parameters (required) method_parameters list parameters define models used, including: - ratio_dis ratio data delegated discovery sub-sample (default: 0.5). - ite_method method estimate individual treatment effect (default: “aipw”) [1]. - learner_ps (SuperLearner) model propensity score estimation (default: “SL.xgboost”, used “aipw”,“bart”,“cf” ITE estimators). - learner_y (SuperLearner) model outcome estimation (default: “SL.xgboost”, used “aipw”,“slearner”,“tlearner” “xlearner” ITE estimators). hyper_params list hyper parameters fine tune method, including: - intervention_vars Intervention-able variables used Rules Generation (default: NULL). - ntrees number decision trees random forest (default: 20). - node_size Minimum size trees’ terminal nodes (default: 20). - max_rules Maximum number candidate decision rules (default: 50). - max_depth Maximum rules length (default: 3). - t_decay decay threshold rules pruning (default: 0.025). - t_ext threshold define generic specific (extreme) rules (default: 0.01). - t_corr threshold define correlated rules (default: 1). - stability_selection Method stability selection selecting rules. vanilla stability selection, error_control stability selection error control stability selection (default: vanilla). - B Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). - subsample Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). - offset Name covariate use offset (.e. “x1”) T-Poisson ITE Estimation. NULL used (default: NULL). - cutoff Threshold defining minimum cutoff value stability scores Stability Selection (default: 0.9). - pfer Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). Additional Estimates (required) ite estimated ITE vector. given, ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"options-for-the-ite-estimation","dir":"Articles","previous_headings":"Arguments > Notes","what":"Options for the ITE estimation","title":"CRE","text":"[1] Options ITE estimation follows: - S-Learner (slearner). - T-Learner (tlearner) - T-Poisson(tpoisson) - X-Learner (xlearner) - Augmented Inverse Probability Weighting (aipw) - Causal Forests (cf) - Causal Bayesian Additive Regression Trees (bart) estimates ITE provided ite additional argument, ITE estimations discovery inference skipped values estimates used instead. ITE estimator requires also outcome learner /propensity score learner SuperLearner package (.e., “SL.lm”, “SL.svm”). models simple classifiers/regressors. default XGBoost algorithm used steps.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"customized-wrapper-for-superlearner","dir":"Articles","previous_headings":"Arguments > Notes","what":"Customized wrapper for SuperLearner","title":"CRE","text":"One can create customized wrapper SuperLearner internal packages. following example providing number cores (e.g., 12) xgboost package shared memory system. use “m_xgboost”, instead “SL.xgboost”.","code":"m_xgboost <- function(nthread = 12, ...) {   SuperLearner::SL.xgboost(nthread = nthread, ...) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"CRE","text":"Example 1 (default parameters) Example 2 (personalized ite estimation) Example 3 (setting parameters) synthetic data sets can generated using generate_cre_dataset().","code":"set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  cre_results <- cre(y, z, X) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\")   y <- dataset[[\"y\"]]   z <- dataset[[\"z\"]]   X <- dataset[[\"X\"]]  ite_pred <- ... # personalized ite estimation cre_results <- cre(y, z, X, ite = ite_pred) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = c(\"x1\",\"x2\",\"x3\",\"x4\"),                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.8,                      pfer = 1,                      B = 10,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"environment-setup","dir":"Articles","previous_headings":"","what":"Environment Setup","title":"Contribution","text":"Please follow steps get copy CRE Github account. Navigate CRE Github repository, top right corner, click Fork button. add clone project Github account. Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): already SSH key, need generate one. Read . Now, can modify codebase track modification. good idea create new branch work codebase. Read following instructions git branching.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"git-branching-model","dir":"Articles","previous_headings":"","what":"Git Branching Model","title":"Contribution","text":"Although, personal repository, can pick branch name, however, order keep consistency also understand working , following convention strongly recommended. project, follow convention proposed Vincent Driessen successful Git branching model post.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"where-to-submit-pull-requests","dir":"Articles","previous_headings":"","what":"Where to submit pull requests?","title":"Contribution","text":"pull requests submitted base repository: fasrc/CRE base: develop branch.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"pull-request-checklist","dir":"Articles","previous_headings":"","what":"Pull request checklist","title":"Contribution","text":"Please run devtools::document(), devtools::load_all() final modifications. Make sure modified code passes checks tests (can run devtools::check() RStudio) PR pass CI reviews can merge . Add line(s) modification NEWS.md file. adding new features, please make sure appropriate documentation added updated. Please clean white spaces. Read .","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"reporting-bugs","dir":"Articles","previous_headings":"","what":"Reporting bugs","title":"Contribution","text":"Please report potential bugs creating new issue sending us email. Please include following information bug report: brief description , expected happen, happened. OS using whether using personal computer HPC cluster. version package installed.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"style-guide","dir":"Articles","previous_headings":"","what":"Style Guide","title":"Contribution","text":"project, follow tidyverse style guide.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"names","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Names","title":"Contribution","text":"File names snake_case ends .R (e.g., create_matching.R) variable names small letter separate _ need (e.g., delta_n) Function names follow snake_case style (e.g., generate_data) Function names follow verb+output convention (e.g., compute_resid)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"spaces-and-indentation","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Spaces and Indentation","title":"Contribution","text":"Indentations two spaces (use tab) Place space around binary operators (e.g., x + y) Place space comma Place space # commenting avoid multiple ### put space opening closing parenthesis Place space () used , , .","code":"#Acceptable: z <- x + y #Not recommended: z<-x+y # (no space) z<- x+y z<-x +y #Acceptable: a <- matrix(c(1:100), nrow = 5) #Not recommended: a <- matrix(c(1:100),nrow = 5) # (no space after comma) a <- matrix( c(1:100), nrow = 5 ) # (extra space after and before parentheses) a<-matrix(c(1:100), nrow = 5) # (no space around unary operator <- ) #Acceptable: # This is a comment #Not recommended: #This is a comment #    This is a comment (more than one space after #) ## This is a comment (multiple #) ###    This is a comment (multiple # and more than one space) #Acceptable: x <- (z + y) #Not recommended: x <- ( z + y ) # (unnecessary space) x <- (z + y ) x <- ( z + y) #Acceptible if (x > 2) {   print(x) } # Not recommended if(x > 2){   print(x) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"other-notes","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Other notes","title":"Contribution","text":"Maximum line length 80 character Use explicit returns Use explicit tags documentation (e.g., @title, @description, …)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"getting-the-code","dir":"Articles","previous_headings":"","what":"Getting the code","title":"Testing the CRE Package","text":"test package, please install package system (R (>= 3.5.0)). can install package following one approaches: Directly GitHub CRAN (recommended) Source Forked Repository (recommended)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-directly-from-github","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package directly from Github","title":"Testing the CRE Package","text":"project, follow successful Git Branching Model. result, develop branch updated branch developers. Use devtools::install_github install package. specify ref, install master (main) branch. Try ?CRE. open package description page help tab (assuming using RStudio).","code":"library(devtools) try(detach(\"package:CRE\", unload = TRUE), silent = TRUE) # if already you have the package, detach and unload it, to have a new install.  install_github(\"NSAPH-Software/CRE\", ref=\"develop\") library(CRE)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-cran","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from CRAN","title":"Testing the CRE Package","text":"Installing package CRAN developing purposes recommended. probably, version CRAN latest version. [Complete section submitting package CRAN]","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-the-source","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from the source","title":"Testing the CRE Package","text":"order install package source, need download source code computer install source. steps: Go package Github repository drop-menu change branch develop. click Code tab click Download Zip tab. Open one files using RStudio, change project directory project directory (Session > Set Working Directory > Project Directory). Load devtools library load CRE.","code":"library(devtools) load_all() ?CRE"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"forking-the-package","dir":"Articles","previous_headings":"Getting the code","what":"Forking the package","title":"Testing the CRE Package","text":"Forking package Github account best option planning installing, testing, modifying, contributing project. Go package Github repository click Fork button top right corner. forking package, Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): Now, can modify codebase track modification. Navigate package folder Install package following Installing package source steps. good idea create new branch work codebase. Read successful Git Branching Model branching convention.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"testing-the-package","dir":"Articles","previous_headings":"","what":"Testing the Package","title":"Testing the CRE Package","text":"Run following command test package.","code":"library(CRE)  # Generate sample data set.seed(1358) dataset <- generate_cre_dataset(n = 1000,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method = \"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\",                       offset = NULL)  hyper_params <- list(intervention_vars = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.01,                      t_corr = 1,                      t_pvalue = 0.05,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 10,                      subsample = 0.5)  # linreg CATE estimation with aipw ITE estimation cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            FASRC Daniela Maria Garcia. Author. Riccardo Cadei. Author. Kwonsang Lee. Author. Falco Joannes Bargagli Stoffi. Author.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Khoshnevis N, Garcia D, Cadei R, Lee K, Bargagli Stoffi F (2023). CRE: Interpretable Discovery Inference Heterogeneous Treatment Effects. R package version 0.2.5, https://github.com/NSAPH-Software/CRE.","code":"@Manual{,   title = {CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},   author = {Naeem Khoshnevis and Daniela Maria Garcia and Riccardo Cadei and Kwonsang Lee and Falco Joannes {Bargagli Stoffi}},   year = {2023},   note = {R package version 0.2.5},   url = {https://github.com/NSAPH-Software/CRE}, }"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"Identifying subgroups study population treatment exposure notably larger smaller effect outcome compared population average crucial social health sciences. estimating conditional average treatment effect (CATE) given pre-specified set covariates common approach, allows estimating causal effects subgroups specified priori researchers. package implements recently developed Causal Rule Ensemble (CRE) algorithm, flexible stable method interpretable discovery estimation heterogeneous treatment effects terms decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"package-details","dir":"","previous_headings":"","what":"Package Details","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"cre() function executes entire CRE algorithm utilizing data set input parameters user provides. divides study data discovery inference subsamples, generating filtering decision rules discovery subsample explain heterogeneity treatment effects. cre() uses inference subsample estimate linear decomposition Conditional Average Treatment Effect (CATE) terms just discovered decision rules, normal equations model ITE estimates. steps, cre() agnostic concerning method used ITE estimation. estimate Individual Treatment Effects (ITE), users can choose various methodologies vary terms speed accuracy. Several ITE estimators internally implemented (.e. AIPW, Causal Forest, Bayesian Causal Forest, Causal BART, S-Learner, T-Learner, X-Learner), customized ITE estimation can also provided input. results collected S3 object containing: number decision rules extracted step, CATE decomposition estimates corresponding uncertainty quantification, list selected parameters, predicted ITEs.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"applications","dir":"","previous_headings":"","what":"Applications","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"CRE package can used diverse theoretical practical applications long working binary exposure treatment level. example, can used education determine teaching methods effective specific student subgroups. marketing, can identify effective strategies different consumer subgroups. healthcare, can used identify patient subgroups benefit particular treatment intervention. can also used studies personalized medicine subgroup patients responds differently provided medicine. public health, CRE method applied discover subgroups vulnerable (resilient) causal effects long-term exposure air pollution mortality.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The CRE package — CRE-package","title":"The CRE package — CRE-package","text":"health social sciences, critically important identify subgroups study population treatment notable heterogeneity causal effects respect average treatment effect. Data-driven discovery heterogeneous treatment effects (HTE) via decision tree methods proposed task. Despite high interpretability, single-tree discovery HTE tends highly unstable find oversimplified representation treatment heterogeneity. accommodate shortcomings, propose Causal Rule Ensemble (CRE), new method discover heterogeneous subgroups ensemble--trees approach. CRE following features: provides interpretable representation HTE; 2) allows extensive exploration complex heterogeneity patterns; 3) guarantees high stability discovery. discovered subgroups defined terms interpretable decision rules, develop general two-stage approach subgroup-specific conditional causal effects estimation, providing theoretical guarantees.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The CRE package — CRE-package","text":"Bargagli-Stoffi, F. J., Cadei, R., Lee, K. Dominici, F. (2023). Causal rule ensemble: Interpretable Discovery Inference Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The CRE package — CRE-package","text":"Naeem Khoshnevis Daniela Maria Garcia Riccardo Cadei Kwonsang Lee Falco Joannes Bargagli Stoffi","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Sensitivity Analysis — analyze_sensitivity","title":"Sensitivity Analysis — analyze_sensitivity","text":"Method analyzing sensitivity estimates causal rule-specific treatment effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sensitivity Analysis — analyze_sensitivity","text":"","code":"analyze_sensitivity(ite_std, rules_matrix_std)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sensitivity Analysis — analyze_sensitivity","text":"ite_std standardized ITE rules_matrix_std standardized causal rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sensitivity Analysis — analyze_sensitivity","text":"list containing results sensitivity analysis","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cre object — autoplot.cre","title":"A helper function for cre object — autoplot.cre","text":"helper function plot cre object using ggplot2 package.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cre object — autoplot.cre","text":"","code":"# S3 method for cre autoplot(object, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cre object — autoplot.cre","text":"object cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cre object — autoplot.cre","text":"Returns ggplot object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_hyper_params","title":"Check input parameters — check_hyper_params","text":"Checks consistency input (hyper) parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_hyper_params","text":"","code":"check_hyper_params(X_names, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_hyper_params","text":"X_names observed covariates names. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_hyper_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_input","title":"Check input parameters — check_input","text":"Checks input parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_input","text":"","code":"check_input(y, z, X, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_input","text":"y observed response vector. z treatment vector. X covariate matrix. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_input","text":"list params might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input data — check_input_data","title":"Check input data — check_input_data","text":"Conducts sanity checks input data.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input data — check_input_data","text":"","code":"check_input_data(y, z, X, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input data — check_input_data","text":"y observed response vector. z treatment vector. X features matrix. ite estimated ITE vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input data — check_input_data","text":"number data samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check method-related parameters — check_method_params","title":"Check method-related parameters — check_method_params","text":"Checks method-related parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check method-related parameters — check_method_params","text":"","code":"check_method_params(y, ite, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check method-related parameters — check_method_params","text":"y observed response vector. ite estimated ITE vector. params list parameters required run method functions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check method-related parameters — check_method_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Causal rule ensemble — cre","title":"Causal rule ensemble — cre","text":"Performs Causal Rule Ensemble data set response variable, treatment variable, various features.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Causal rule ensemble — cre","text":"","code":"cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Causal rule ensemble — cre","text":"y observed response vector. z treatment vector. X covariate matrix (data frame). provided numerical values. method_params list parameters define models used, including: Parameters Honest Splitting ratio_dis: ratio data delegated rules discovery (default: 0.5). Parameters Discovery Inference ite_method: method ITE (pseudo-outcome) estimation (default: \"aipw\", options: \"aipw\" Augmented Inverse Probability Weighting, \"cf\" Causal Forest, \"bart\" Causal Bayesian Additive Regression Trees, \"slearner\" S-Learner, \"tlearner\" T-Learner, \"xlearner\" X-Learner, \"tpoisson\" T-Learner Poisson regression). learner_ps: model propensity score estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"bart\", \"cf\" ITE estimators). learner_y: model outcome estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"slearner\", \"tlearner\" \"xlearner\" ITE estimators). hyper_params list hyper parameters fine-tune method, including: General hyper parameters intervention_vars: Array intervention-able covariates names used Rules Generation. Empty null array means covariates considered intervention-able (default: NULL). ntrees: number decision trees random forest (default: 20). node_size: Minimum size trees' terminal nodes (default: 20). max_rules: Maximum number generated candidates rules (default: 50). max_depth: Maximum rules length (default: 3). t_decay: decay threshold rules pruning. Higher values carry aggressive pruning (default: 0.025). t_ext: threshold truncate generic specific (extreme) rules (default: 0.01, range: [0, 0.5)). t_corr: threshold define correlated rules (default: 1, range: [0,+inf)). stability_selection: Method stability selection selecting rules. \"vanilla\" stability selection, \"error_control\" stability selection error control  \"\" stability selection (default: \"vanilla\"). B: Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). subsample: Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). Method specific hyper parameters offset: Name covariate use offset (.e., \"x1\") T-Poisson ITE estimation. Use NULL offset used (default: NULL). cutoff:  Threshold (percentage) defining minimum cutoff value stability scores Stability Selection (default: 0.9). pfer: Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). ite estimated ITE vector. given ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Causal rule ensemble — cre","text":"S3 object composed : M number Decision Rules extracted step, CATE data.frame Conditional Average Treatment Effect decomposition estimates corresponding uncertainty quantification, method_params list method parameters, hyper_params list hyper parameters, rules list rules (implicit form) decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Causal rule ensemble — cre","text":"intervention_vars provided, important note individual treatment effect still computed using covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Causal rule ensemble — cre","text":"","code":"# \\donttest{ set.seed(123) dataset <- generate_cre_dataset(n = 400,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = NULL,                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 20,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) #> Loading required package: nnls #> Registered S3 method overwritten by 'randomForest': #>   method      from #>   plot.margin RRF  # }"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":null,"dir":"Reference","previous_headings":"","what":"The Causal Rule Ensemble Crossfit — cre_crossfit","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"Performs Causal Rule Ensemble twice dataset response variable, treatment variable, various features, swapping discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"cre_crossfit(   y,   z,   X,   ite_method_dis,   include_ps_dis = NA,   ps_method_dis = \"SL.xgboost\",   or_method_dis = NA,   ite_method_inf,   include_ps_inf = NA,   ps_method_inf = \"SL.xgboost\",   or_method_inf = NA,   ntrees_rf,   ntrees_gbm,   min_nodes,   max_nodes,   t,   q,   rules_method = NA,   include_offset = FALSE,   offset_name = NA,   cate_method = \"DRLearner\",   cate_SL_library = \"SL.xgboost\",   filter_cate = FALSE )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"y observed response vector z treatment vector X covariate matrix ite_method_dis method estimate discovery sample ITE include_ps_dis whether include propensity score estimate covariate discovery ITE estimation, considered BART, XBART, CF ps_method_dis estimation model propensity score discovery subsample or_method_dis estimation model outcome regressions estimate_ite_aipw discovery subsample ite_method_inf method estimate inference sample ITE include_ps_inf whether include propensity score estimate covariate inference ITE estimation, considered BART, XBART, CF ps_method_inf estimation model propensity score inference subsample or_method_inf estimation model outcome regressions estimate_ite_aipw inference subsample ntrees_rf number decision trees randomForest ntrees_gbm number decision trees gradient boosting min_nodes minimum size trees' terminal nodes max_nodes maximum size trees' terminal nodes t common support used generating causal rules matrix q selection threshold used selecting causal rules rules_method method selecting causal rules binary outcomes include_offset whether include offset estimating ITE, poisson offset_name name offset, included cate_method method estimate CATE values cate_SL_library library used cate_method set DRLearner filter_cate whether filter rules p-value <= 0.05","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"S3 object containing matrix averaged Conditional Average Treatment Effect estimates two CRE iterations","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  cre_cross_results <- cre_crossfit(y = dataset[[\"y\"]], z = dataset[[\"z\"]],                                   X = as.data.frame(dataset[[\"X\"]]),                                   ite_method_dis = \"bart\",                                   include_ps_dis = TRUE,                                   ite_method_inf = \"bart\",                                   include_ps_inf = TRUE,                                   ntrees_rf = 100, ntrees_gbm = 50,                                   min_nodes = 20, max_nodes = 5,                                   t = 0.025, q = 0.8) #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> Conducting Discovery Subsample Analysis 1 #> 411 rules (length<=15) were extracted from the first 100 trees. #> 150 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 1 #> Conducting Discovery Subsample Analysis 2 #> 493 rules (length<=15) were extracted from the first 100 trees. #> 100 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 2"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"Discard rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"","code":"discard_anomalous_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"Discard redundant correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"","code":"discard_correlated_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover causal decision rules — discover_causal_rules","title":"Discover causal decision rules — discover_causal_rules","text":"Discovers Causal Decision Rules randomization-based tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover causal decision rules — discover_causal_rules","text":"","code":"discover_causal_rules(   rules_matrix,   rules_list,   ite,   stability_selection,   cutoff,   pfer,   penalty_rl )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover causal decision rules — discover_causal_rules","text":"rules_matrix causal rules matrix. rules_list vector causal rules. ite estimated ITE. stability_selection Whether using stability selection select causal rules. cutoff Threshold (percentage) defining minimum cutoff value stability scores. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). penalty_rl Order penalty rules length LASSO Causal Rules Discovery (.e. 0: penalty, 1: rules_length, 2: rules_length^2)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover causal decision rules — discover_causal_rules","text":"List Causal Decision Rules discovered","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover rules — discover_rules","title":"Discover rules — discover_rules","text":"Discover minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover rules — discover_rules","text":"","code":"discover_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover rules — discover_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover rules — discover_rules","text":"minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Conditional Average Treatment Effect — estimate_cate","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"Estimates Conditional Average Treatment Effect (CATE) linearly modeling Individual Treatment Effect set rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"","code":"estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"rules_matrix rules matrix. rules_explicit list select rules terms covariate names. ite estimated ITEs. B number bootstrap samples uncertainty quantification estimation. subsample bootstrap ratio subsample uncertainty quantification estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"list 2 elements: summary: data frame summarizing CATE linear decomposition: Rule: rule name, Estimate: linear contribution CATE, CI_lower: lower bound 95% confidence interval estimate, CI_upper: upper bound 95% confidence interval estimate, P_Value: p-value (Z-test). model: linear model CATE-ATE estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"Estimates Individual Treatment Effect given response vector, treatment vector, covariate matrix, desired algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"","code":"estimate_ite(y, z, X, ite_method, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"y observed response vector. z observed treatment vector. X covariate matrix. ite_method method estimating Individual Treatment Effect. methods requires additional parameters. parameters mentioned indented blocks method definitions provided end parameters list. \"slearner\": S-Learner. - learner_y \"tlearner\": T-Learner. learner_y \"tpoisson\": T-Poisson. offset \"xlearner\": X-Learner. learner_y \"aipw\": Augmented Inverse Probability Weighting. learner_ps  learner_y \"bart\": Bayesian Additive Regression Trees. learner_ps \"cf\": Causal Forest. learner_ps ... Additional parameters passed different models.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"list ITE estimates.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"additional-parameters","dir":"Reference","previous_headings":"","what":"Additional parameters","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"learner_ps: estimation method propensity score. includes libraries SuperLearner package. learner_y: estimation model outcome. includes libraries SuperLearner package. offset: Name covariate use offset (.e. \"x1\") Poisson ITE Estimation. NULL offset used.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"Estimates Individual Treatment Effect using Augmented Inverse Probability Weighting given response vector, treatment vector, features matrix, estimation model propensity score estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"","code":"estimate_ite_aipw(y, z, X, learner_ps = \"SL.xgboost\", learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"y observed response vector. z treatment vector. X features matrix. learner_ps estimation model propensity score. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"Estimates Individual Treatment Effect using Bayesian Additive Regression Trees given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"","code":"estimate_ite_bart(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"y observed response vector. z treatment vector. X features matrix. learner_ps Method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"number samples number burn set default equal 500.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"Estimates Individual Treatment Effect using Bayesian Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"","code":"estimate_ite_bcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"y observed response vector. z treatment vector. X features matrix. ps_method method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"Estimates Individual Treatment Effect using Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"","code":"estimate_ite_cf(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"y observed response vector. z treatment vector. X features matrix. learner_ps method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"Estimates Individual Treatment Effect (ITE) using Inverse Propensity Weighting (IPW) given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"","code":"estimate_ite_ipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"Method estimating Individual Treatment Effect using Outcome Regression given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"estimate_ite_or(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"y observed response vector z treatment vector X features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  # Initialize parameters y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- as.data.frame(dataset[[\"X\"]])  ite_list <- estimate_ite_or(y, z, X) #> *****Into main of wbart #> *****Data: #> data:n,p,np: 388, 10, 612 #> y1,yn: -0.694294, 1.362455 #> x1,x[n*p]: 1.000000, 1.000000 #> xp1,xp[np*p]: 0.000000, 0.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.118817,3.000000,0.250422 #> *****sigma: 1.133838 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000 #> *****Into main of wbart #> *****Data: #> data:n,p,np: 612, 10, 388 #> y1,yn: -0.485955, 0.857738 #> x1,x[n*p]: 0.000000, 0.000000 #> xp1,xp[np*p]: 1.000000, 1.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.139249,3.000000,0.168737 #> *****sigma: 0.930723 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"Estimates Individual Treatment Effect (ITE) using Outcome Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"","code":"estimate_ite_oreg(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"y observed response vector. z treatment vector. X features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"Estimates Individual Treatment Effect using Poisson Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"","code":"estimate_ite_poisson(y, z, X, X_names, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"y observed response vector. z treatment vector. X features matrix. X_names vector includes names covariates. offset name covariate use offset (.e. 'x1') model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"Estimates Individual Treatment Effect using Stabilized Inverse Propensity Weighting given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"","code":"estimate_ite_sipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"Estimates Individual Treatment Effect using S-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"","code":"estimate_ite_slearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"Estimates Individual Treatment Effect using T-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"","code":"estimate_ite_tlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"Estimates Individual Treatment Effect using Poisson regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"","code":"estimate_ite_tpoisson(y, z, X, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"y observed response vector. z treatment vector. X features matrix. offset name covariate use offset (.e. “x1”) model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"","code":"estimate_ite_xbart(y, z, X, include_ps, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"y observed response vector z treatment vector X features matrix include_ps whether include propensity score estimate covariate ITE estimation ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Causal Forest given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"","code":"estimate_ite_xbcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"y observed response vector z treatment vector X features matrix ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"Estimates Individual Treatment Effect using X-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"","code":"estimate_ite_xlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the propensity score — estimate_ps","title":"Estimate the propensity score — estimate_ps","text":"Estimates Propensity Score given treatment vector features data frame.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the propensity score — estimate_ps","text":"","code":"estimate_ps(z, X, ps_method = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the propensity score — estimate_ps","text":"z treatment vector. X features data frame. ps_method estimation model propensity score (default: SL.xgboost).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the propensity score — estimate_ps","text":"vector propensity score estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":null,"dir":"Reference","previous_headings":"","what":"Discovery (performance) evaluation — evaluate","title":"Discovery (performance) evaluation — evaluate","text":"Computes different metrics discovery evaluation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discovery (performance) evaluation — evaluate","text":"","code":"evaluate(ground_truth, prediction)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discovery (performance) evaluation — evaluate","text":"ground_truth List true (CDR/EM) values. prediction List predicted (CDR/EM) values.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discovery (performance) evaluation — evaluate","text":"Intersection union, precision, recall.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract effect modifiers — extract_effect_modifiers","title":"Extract effect modifiers — extract_effect_modifiers","text":"Extracts effect modifiers list (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract effect modifiers — extract_effect_modifiers","text":"","code":"extract_effect_modifiers(rules_list, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract effect modifiers — extract_effect_modifiers","text":"rules_list list (causal) decision rules. X_names list covariate names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract effect modifiers — extract_effect_modifiers","text":"list effect modifiers.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract (causal) decision rules — extract_rules","title":"Extract (causal) decision rules — extract_rules","text":"Extracts causal rules random forest gradient boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract (causal) decision rules — extract_rules","text":"","code":"extract_rules(treelist, X, max_depth, digits = 2)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract (causal) decision rules — extract_rules","text":"treelist list decision trees. X Features matrix. max_depth number top levels tree considered. digits Number digits rounding decision rules extract conditions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract (causal) decision rules — extract_rules","text":"vector (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter correlated rules — filter_correlated_rules","title":"Filter correlated rules — filter_correlated_rules","text":"Discards highly correlated rules (.e., \\(Cov(rule_1,rule_2) > t_{corr}\\)).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter correlated rules — filter_correlated_rules","text":"","code":"filter_correlated_rules(rules_matrix, rules_list, t_corr)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter correlated rules — filter_correlated_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_corr threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter correlated rules — filter_correlated_rules","text":"rules matrix without highly correlated rules (columns).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter extreme decision rules — filter_extreme_rules","title":"Filter extreme decision rules — filter_extreme_rules","text":"Discards rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter extreme decision rules — filter_extreme_rules","text":"","code":"filter_extreme_rules(rules_matrix, rules_list, t_ext)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_ext threshold [0, 0.5) range truncate generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules matrix without rare/common rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"Filters irrelevant decision rules. irrelevant rules interpreted error increase removing variable-value pair decision rules (see “Interpreting tree ensembles inTrees package” Houtao Deng, 2019).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"","code":"filter_irrelevant_rules(rules, X, ite, t_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"rules list rules. X features matrix. ite estimated ITE. t_decay decay threshold rules pruning.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"list “relevant” rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate causal rules — generate_causal_rules","title":"Generate causal rules — generate_causal_rules","text":"Generates causal decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate causal rules — generate_causal_rules","text":"","code":"generate_causal_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate causal rules — generate_causal_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate causal rules — generate_causal_rules","text":"vector causal rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate CRE synthetic data — generate_cre_dataset","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Generates synthetic data sets run simulation causal inference experiments composed outcome vector (y), treatment vector (z), covariates matrix (X), unobserved individual treatment effects vector (ite). arguments specify data set characteristic, including number individuals (n), number covariates (p), correlation within covariates (rho),  number decision rules (n_rules) decomposing Conditional Average Treatment Effect (CATE), treatment effect magnitude (effect_size), confounding mechanism (confounding), whether covariates outcomes binary continuous (binary_covariates, binary_outcome).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"generate_cre_dataset(   n = 1000,   rho = 0,   n_rules = 2,   p = 10,   effect_size = 2,   binary_covariates = TRUE,   binary_outcome = TRUE,   confounding = \"no\" )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate CRE synthetic data — generate_cre_dataset","text":"n integer number represents number observations. Non-integer values converted integer number. rho positive double number represents correlation within covariates (default: 0, range: [0,1)). n_rules number causal rules (default: 2, range: {1,2,3,4}). p number covariates (default: 10). effect_size treatment effect size magnitude (default: 2, range: \\(\\geq\\)0). binary_covariates Whether use binary continuous covariates (default: TRUE). binary_outcome Whether use binary continuous outcomes (default: TRUE). confounding continuous outcome, add confounding variables: \"lin\" linear confounding, \"nonlin\" non-linear confounding, \"\" confounding (default).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate CRE synthetic data — generate_cre_dataset","text":"list, representing generated synthetic data set, containing: y outcome vector, z treatment vector, X covariates matrix, ite individual treatment vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate CRE synthetic data — generate_cre_dataset","text":"covariates matrix generated specified correlation among individuals, covariate sampled either Bernoulli(0.5) binary, Gaussian(0,1) continuous. treatment vector sampled Bernoulli(\\(\\frac{1}{1+ \\exp(1-x_1+x_2-x_3)}\\)), enforcing treatment assignment probabilities function observed covariates. potential outcomes (\\(y(0)\\) \\(y(1)\\)) sampled Bernoulli binary, Gaussian (standard deviation equal 1) continuous. mean equal confounding term (null, linear non-linear always null binary outcome) plus 1-4 decision rules weighted treatment effect magnitude. two potential outcomes characterizes CATE (unobserved individual treatment effects vector) sum different additive contributions decision rules considered (plus intercept). final expression CATE depends treatment effect magnitude number decision rules considered. 4 decision rules : Rule 1: \\(1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x})\\) Rule 2: \\(1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})\\) Rule 3: \\(1\\{x_4 \\leq 0.5\\}(\\textbf{x})\\) Rule 4: \\(1\\{x_5 \\leq 0.5; x_7 > 0.5; x_8 \\leq 0.5\\}(\\textbf{x})\\) corresponding additive average treatment effect (AATE) equal : Rule 1: \\(-\\) effect_size, Rule 2: \\(+\\) effect_size, Rule 3: \\(- 0.5 \\cdot\\) effect_size, Rule 4: \\(+ 2 \\cdot\\) effect_size. example, setting effect_size=4 n_rules=2: $$\\text{CATE}(\\textbf{x}) = -4 \\cdot 1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x}) + 4 \\cdot 1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})$$ final outcome vector y finally computed combining potential outcomes according treatment assignment.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Set covariates domain (binary_covariates) outcome domain (binary_outcome) according experiment interest. Increase complexity heterogeneity discovery: decreasing sample size (n), adding correlation among covariates (rho), increasing number rules (n_rules), increasing number covariates (p), decreasing absolute value causal effect (effect_size), adding linear -linear confounders (confounding).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"set.seed(123) dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary_covariates = TRUE,                                 binary_outcome = TRUE, confounding = \"no\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules — generate_rules","title":"Generate rules — generate_rules","text":"Generates list rules characterizing heterogeneity Conditional Average Treatment Effect (CATE) tree-based methods (.e., random forest).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules — generate_rules","text":"","code":"generate_rules(X, ite, ntrees, node_size, max_rules, max_depth)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules — generate_rules","text":"X covariate matrix. ite vector estimated ITE. ntrees number decision trees random forest algorithm. node_size Minimum size trees' terminal nodes. max_rules Maximum number candidate decision rules. max_depth Maximum rules length.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules — generate_rules","text":"list rules (names).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules matrix — generate_rules_matrix","title":"Generate rules matrix — generate_rules_matrix","text":"Generates rules matrix feature covariate matrix vector rules. number rows rules_matrix equal number samples X, number columns equal number rules rules_list. element rules_matrix corresponds specific data sample rule. data sample satisfies rule, corresponding element rules_matrix set 1. Otherwise, element set 0.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules matrix — generate_rules_matrix","text":"","code":"generate_rules_matrix(X, rules_list)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules matrix — generate_rules_matrix","text":"X Features matrix. rules_list vector rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules matrix — generate_rules_matrix","text":"causal rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Logger settings — get_logger","title":"Get Logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":null,"dir":"Reference","previous_headings":"","what":"Honest splitting — honest_splitting","title":"Honest splitting — honest_splitting","text":"Splits data discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Honest splitting — honest_splitting","text":"","code":"honest_splitting(y, z, X, ratio_dis, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Honest splitting — honest_splitting","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis double number indicating ratio data delegated discovery sub-sample. ite vector estimated ITE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Honest splitting — honest_splitting","text":"list containing discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret rules — interpret_rules","title":"Interpret rules — interpret_rules","text":"Replaces column numbers rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret rules — interpret_rules","text":"","code":"interpret_rules(rules, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret rules — interpret_rules","text":"rules vector rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret rules — interpret_rules","text":"list explicit (human-readable) rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret select rules — interpret_select_rules","title":"Interpret select rules — interpret_select_rules","text":"Replaces column numbers select rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret select rules — interpret_select_rules","text":"","code":"interpret_select_rules(select_rules_dis, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret select rules — interpret_select_rules","text":"select_rules_dis vector select causal rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret select rules — interpret_select_rules","text":"vector select causal rules interpretable.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"","code":"lasso_rules_filter(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q Number (unique) selected rules per subsample stability selection. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cre class — plot.cre","title":"Extend generic plot functions for cre class — plot.cre","text":"wrapper function extend generic plot functions cre class.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cre class — plot.cre","text":"","code":"# S3 method for cre plot(x, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cre class — plot.cre","text":"x cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cre class — plot.cre","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict individual treatment effect via causal rule ensemble — predict.cre","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"Predicts individual treatment effect via causal rule ensemble algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"","code":"# S3 method for cre predict(object, X, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"object cre object running CRE function. X covariate matrix (data.frame) ... Additional arguments passed customize prediction.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"array estimated Individual Treatment Effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for the CRE object — print.cre","title":"Extend print function for the CRE object — print.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for the CRE object — print.cre","text":"","code":"# S3 method for cre print(x, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for the CRE object — print.cre","text":"x cre object running CRE function. verbose Set level results description details: 0 results summary, 1 results parameters summary, 2 results parameters rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for the CRE object — print.cre","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune (Causal) Decision Rules — prune_rules","title":"Prune (Causal) Decision Rules — prune_rules","text":"Method pruning (Causal) Decision Rules extracted Random Forest Gradient Boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune (Causal) Decision Rules — prune_rules","text":"","code":"prune_rules(rules, X, ite_std, max_decay, type_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune (Causal) Decision Rules — prune_rules","text":"rules list decision rules. X features matrix. ite_std standardized ITE. max_decay Decay Threshold pruning rules. type_decay Decay Type pruning rules (1: relative error; 2: error).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune (Causal) Decision Rules — prune_rules","text":"list (Causal) Decision Rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select Causal Rules — select_causal_rules","title":"Select Causal Rules — select_causal_rules","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select Causal Rules — select_causal_rules","text":"","code":"select_causal_rules(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select Causal Rules — select_causal_rules","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q selection threshold used selecting causal rules. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select Causal Rules — select_causal_rules","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select rules — select_rules","title":"Select rules — select_rules","text":"Given set rules, selects minimal set linearly decomposing Conditional Average Treatment Effect (CATE) LASSO (optionally Stability Selection).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select rules — select_rules","text":"","code":"select_rules(rules_matrix, rules, ite, stability_selection, cutoff, pfer, B)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select rules — select_rules","text":"rules_matrix rules matrix. rules list rules. ite estimated ITE. stability_selection Stability selection method. cutoff Threshold (percentage) defining minimum cutoff value stability scores. stability selection. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). stability selection. B Number bootstrap samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select rules — select_rules","text":"minimal set rules linearly decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Logger settings — set_logger","title":"Set Logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"CRE.log\", logger_level = \"INFO\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: CRE.log) logger_level log level. log level set, log levels also activated (implemented). Available levels include: TRACE: Provides verbose detailed logging, including steps taken achieve result, often used debugging. Activating TRACE also enable DEBUG, INFO, SUCCESS, WARN, ERROR, FATAL logs. DEBUG: Provides detailed logging flow application, used mostly developers understand potential issues. Activating DEBUG also enable INFO, SUCCESS, WARN, ERROR, FATAL logs. INFO (Default): Standard messages inform user normal operation system. Activating INFO also enable SUCCESS, WARN, ERROR, FATAL logs. SUCCESS: Messages indicating successful completion particular operation task. Activating SUCCESS also enable WARN, ERROR, FATAL logs. WARN: Warning messages events might cause problems future, yet errors. Activating WARN also enable ERROR FATAL logs. ERROR: Reports error due system may able achieve functionality, application halt. Activating ERROR also enable FATAL logs. FATAL: Reports severe error events presumably lead application abort.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set Logger settings — set_logger","text":"Log levels specified developers initial implementation. Future developers contributors can leverage log levels better capture document application's processes events.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Logger settings — set_logger","text":"","code":"set_logger(\"Debug\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Data — split_data","title":"Split Data — split_data","text":"Splits data discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Data — split_data","text":"","code":"split_data(y, z, X, ratio_dis)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Data — split_data","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis ratio data delegated discovery subsample","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Data — split_data","text":"list containing discovery inference subsamples","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardize Rules Matrix — standardize_rules_matrix","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardize (.e. mean=0 stdev=1) rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"","code":"standardize_rules_matrix(rules_matrix)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"rules_matrix rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardized rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of CRE object — summary.cre","title":"Print summary of CRE object — summary.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of CRE object — summary.cre","text":"","code":"# S3 method for cre summary(object, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of CRE object — summary.cre","text":"object cre object running CRE function. verbose Set level results description details: results summary 0, results+parameters summary 1, results+parameters+rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of CRE object — summary.cre","text":"summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":null,"dir":"Reference","previous_headings":"","what":"Take 1 — take1","title":"Take 1 — take1","text":"Generate list indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take 1 — take1","text":"","code":"take1(len)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take 1 — take1","text":"len length value","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take 1 — take1","text":"vector indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take 1 — take1","text":"","code":"ind <- CRE:::take1(10)"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-5","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.5 (2023-12-6)","text":"Add (vanilla) Stability Selection (without Error Control). max_rules hyper parameters max rules filtering. Uncertainty Quantification estimation bootstrapping. B hyper-parameter, subsample hyper-parameter. rules(implicit form) cre() function return. predict() function ITE estimation via CRE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-5","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.5 (2023-12-6)","text":"Type stability_selection binary -> string (‘’,‘vanilla’,‘error_control’). Unify ntrees_gbm hyper-parameter ntrees_gbm hyper-parameter ntrees hyper-parameter. rules generation retrieve decision rules also internal nodes, just terminal nodes. ite_method_dis, ite_method_inf method-parameter -> ite_method. ps_method_dis, ps_method_inf method-parameter -> learner_ps. oreg_method_dis, oreg_method_inf method-parameter -> learner_y.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-5","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.5 (2023-12-6)","text":"max_nodes hyper-parameter. Remove rules generation Generalized Boosted Regression. replace hyper-parameter. penalty_rl hyper-parameter. t_pvalue hyper-parameter. ite_pred cre() function return.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.5 (2023-12-6)","text":"Error saving covariates name CRE result using intervention_vars.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-024-2023-6-14","dir":"Changelog","previous_headings":"","what":"CRE 0.2.4 (2023-6-14)","title":"CRE 0.2.4 (2023-6-14)","text":"CRAN release: 2023-06-14","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-4","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.4 (2023-6-14)","text":"Method paper description updated.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-023-2023-4-27","dir":"Changelog","previous_headings":"","what":"CRE 0.2.3 (2023-4-27)","title":"CRE 0.2.3 (2023-4-27)","text":"CRAN release: 2023-04-27","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-3","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.3 (2023-4-27)","text":"Bayesian Causal Forest (bcf) ITE estimator supported.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-022-2023-4-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.2 (2023-4-17)","title":"CRE 0.2.2 (2023-4-17)","text":"CRAN release: 2023-04-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-2","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.2 (2023-4-17)","text":"Fixed failing unit tests specific operating systems.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-021-2023-3-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.1 (2023-3-17)","title":"CRE 0.2.1 (2023-3-17)","text":"CRAN release: 2023-03-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.1 (2023-3-17)","text":"Replace BATE ATE CATE Linear Decomposition. Update plot() function (remove ATE, old BATE, explicit AATEs).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.1 (2023-3-17)","text":"Code Conduct.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.1 (2023-3-17)","text":"Causal Tree benchmark functional tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.1 (2023-3-17)","text":"Rank-Deficient Rule Matrix Issue (redundant rules). Intervention Variables Filtering (ordered filtering).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-020-2023-1-19","dir":"Changelog","previous_headings":"","what":"CRE 0.2.0 (2023-1-19)","title":"CRE 0.2.0 (2023-1-19)","text":"CRAN release: 2023-01-19","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.0 (2023-1-19)","text":"offset method-parameter -> hyper-parameter estimate_ite_poisson function -> estimate_ite_tpoisson max_dacay hyper-parameter -> t_decay. interpret_select_rules function -> interpret_rules. generate_causal_rules function -> discover_rules. discover_causal_rules function ->select_rules. offset_name method parameter -> offset. Hyper method parameters required arguments cre. cre object: added parameters ite estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.0 (2023-1-19)","text":"Synthetic data set 1 3 rules (generate_cre_dataset). S-Learner (slearner) method ITE estimation. T-Learner (tlearner) method ITE estimation. X-Learner (xlearner) method ITE estimation. Rules Selection description summary.cre. verbose parameter summary.cre. ite, additional cre input parameter use personalized ite estimations. Default values hyper parameters. Default values method parameters. Simulation experiments estimation (estimation.R). Simulation experiments discovery (discovery.R). extract_effect_modifiers function (utility performance evaluation). evaluate function discovery evaluation. confounding parameter generate_cre_dataset set confounding type. ite_pred model CRE results. binary_covariates parameter generate_cre_dataset set covariates domain.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.0 (2023-1-19)","text":"include_ps_inf method-parameter. include_ps_dis method-parameter. oreg method ITE estimation. ipw method ITE estimation. sipw method ITE estimation. ITE standard deviation estimation. type_decay hyper-parameter. Keep linreg CATE estimation (remove cate_method cate_SL_library parameters). method_params hyper_params additional parameters summary.cre. ite standardization Rules Generation. random_state parameter. include_offset method parameter.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.0 (2023-1-19)","text":"Rules Generation Issue (set rules length fix bootstrapping).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-011-2022-10-18","dir":"Changelog","previous_headings":"","what":"CRE 0.1.1 (2022-10-18)","title":"CRE 0.1.1 (2022-10-18)","text":"CRAN release: 2022-10-22","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.1 (2022-10-18)","text":"binary parameter generate_cre_dataset -> binary_outcome . filter_cate hyper-parameter -> t_pvalue. t_anom hyper-parameter -> t_ext. effect_modifier hyper-parameter -> intervention_vars. lasso_rules_filter function -> discover_causal_rules. split_data function -> honest_splitting. prune_rules function -> `filter_irrelevant_rules. discard_correlated_rules function -> filter_correlated_rules. discard_anomalous_rules function -> filter_extreme_rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.1 (2022-10-18)","text":"Weighted LASSO Causal Rules Discovery (penalty_rl hyper-parameter).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-010-2022-10-17","dir":"Changelog","previous_headings":"","what":"CRE 0.1.0 (2022-10-17)","title":"CRE 0.1.0 (2022-10-17)","text":"CRAN release: 2022-10-18","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.0 (2022-10-17)","text":"Update examples tests functions. q hyper-parameter -> cutoff. pfer_val hyper-parameter -> pfer. select_causal_rules function -> lasso_rules_filter. t hyper-parameter -> t_anom. Separate standardization, remove filtering generate_rules_matrix function. summary.cre function describe results. min_nodes hyper-parameter -> node_size (randomForest convention). cre returns S3 object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.0 (2022-10-17)","text":"Examples tests functions. prune_rules function discard un-predictive rules. discard_anomalous_rules function discard anomalous rules (see t_corr hyper-parameter.). discard_correlated_rules function discard correlated rules (see t_anom hyper-parameter). effect_modifiers parameter generate_rules function covariates filtering. generate_causal_rules function. Helper function SuperLearner package propensity score estimation estimate_ite_xyz. Five methods CATE estimation (poisson, DRLearner, bart-baggr, cf-means, linreg) estimate_cate function. (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) method-parameters complement SuperLearner package. cate_method method-parameter select CATE estimation method. filter_cate method-parameter estimation filtering. p parameter (generate_cre_dataset function) set number covariates. replace parameter (generate_rules function) allow bootstrapping. cre.print generic function print cre S3 object results. cre.summary generic functions summarize cre S3 object Results. check_input function isolate input checks. estimate_ite_aipw function augmented inverse propensity weighting. plot.cre generic function plot cre S3 object results. test-cre_functional.R test functionality package. stability_selection function causal rules selection.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-1-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.1.0 (2022-10-17)","text":"estimate_ite_blp function. take1() function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.1.0 (2022-10-17)","text":"Undesired ‘’ Decision Rule Issue. Causal Rule Selected Issue.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.0.1 (2021-10-20)","text":"estimate_cate include two methods estimating CATE values. cre added initial checks binary outcome whether include propensity score ITE estimation. estimate_ite_xyz conduct propensity score estimation using helper function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.0.1 (2021-10-20)","text":"Example generate_cre_dataset. set_logger get_logger. check_input_data function. generate_cre_dataset function generate synthetic data testing package. test-generate_cre_dataset function test. estimate_ps function estimate propensity score. estimate_ite_xbart function generate ITE estimates using accelerated BART. estimate_ite_xbcf function generate ITE estimates using accelerated BCF. analyze_sensitivity function conduct sensitivity analysis unmeasured confounding. cre function perform entire Causal Rule Ensemble method. estimate_cate function generate CATE estimates ITE estimates select rules. estimate_ite function generate ITE estimates using user-specified method (calls estimate_ite_xyz functions). estimate_ite_bart function generate ITE estimates using BART. estimate_ite_bcf function generate ITE estimates using Bayesian Causal Forests. estimate_ite_cf function generate ITE estimates using Causal Forests. estimate_ite_ipw function generate ITE estimates using IPW. estimate_ite_or function generate ITE estimates using Outcome Regression. estimate_ite_sipw function generate ITE estimates using SIPW. extract_rules function extract list causal rules randomForest GBM models. generate_rules function generate causal rule models using randomForest GBM methods. generate_rules_matrix function convert list causal rules matrix. select_causal_rules function apply penalized regression causal rules. select important ones. split_data function split input data discovery inference subsamples. take1 function create subsample indices.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-0-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.0.1 (2021-10-20)","text":"seed argument generate_cre_datase function.","code":""}]
+[{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement nkhoshnevis@g.harvard.edu. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"CRE","text":"Installing CRAN. Installing latest developing version. Import.","code":"install.packages(\"CRE\") library(devtools) install_github(\"NSAPH-Software/CRE\", ref = \"develop\") library(\"CRE\")"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"arguments","dir":"Articles","previous_headings":"","what":"Arguments","title":"CRE","text":"Data (required) y observed response/outcome vector (binary continuous). z treatment/exposure/policy vector (binary). X covariate matrix (binary continuous). Parameters (required) method_parameters list parameters define models used, including: - ratio_dis ratio data delegated discovery sub-sample (default: 0.5). - ite_method method estimate individual treatment effect (default: “aipw”) [1]. - learner_ps (SuperLearner) model propensity score estimation (default: “SL.xgboost”, used “aipw”,“bart”,“cf” ITE estimators). - learner_y (SuperLearner) model outcome estimation (default: “SL.xgboost”, used “aipw”,“slearner”,“tlearner” “xlearner” ITE estimators). hyper_params list hyper parameters fine tune method, including: - intervention_vars Intervention-able variables used Rules Generation (default: NULL). - ntrees number decision trees random forest (default: 20). - node_size Minimum size trees’ terminal nodes (default: 20). - max_rules Maximum number candidate decision rules (default: 50). - max_depth Maximum rules length (default: 3). - t_decay decay threshold rules pruning (default: 0.025). - t_ext threshold define generic specific (extreme) rules (default: 0.01). - t_corr threshold define correlated rules (default: 1). - stability_selection Method stability selection selecting rules. vanilla stability selection, error_control stability selection error control stability selection (default: vanilla). - B Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). - subsample Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). - offset Name covariate use offset (.e. “x1”) T-Poisson ITE Estimation. NULL used (default: NULL). - cutoff Threshold defining minimum cutoff value stability scores Stability Selection (default: 0.9). - pfer Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). Additional Estimates (required) ite estimated ITE vector. given, ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"options-for-the-ite-estimation","dir":"Articles","previous_headings":"Arguments > Notes","what":"Options for the ITE estimation","title":"CRE","text":"[1] Options ITE estimation follows: - S-Learner (slearner). - T-Learner (tlearner) - T-Poisson(tpoisson) - X-Learner (xlearner) - Augmented Inverse Probability Weighting (aipw) - Causal Forests (cf) - Causal Bayesian Additive Regression Trees (bart) estimates ITE provided ite additional argument, ITE estimations discovery inference skipped values estimates used instead. ITE estimator requires also outcome learner /propensity score learner SuperLearner package (.e., “SL.lm”, “SL.svm”). models simple classifiers/regressors. default XGBoost algorithm used steps.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"customized-wrapper-for-superlearner","dir":"Articles","previous_headings":"Arguments > Notes","what":"Customized wrapper for SuperLearner","title":"CRE","text":"One can create customized wrapper SuperLearner internal packages. following example providing number cores (e.g., 12) xgboost package shared memory system. use “m_xgboost”, instead “SL.xgboost”.","code":"m_xgboost <- function(nthread = 12, ...) {   SuperLearner::SL.xgboost(nthread = nthread, ...) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"CRE","text":"Example 1 (default parameters) Example 2 (personalized ite estimation) Example 3 (setting parameters) synthetic data sets can generated using generate_cre_dataset().","code":"set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  cre_results <- cre(y, z, X) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\")   y <- dataset[[\"y\"]]   z <- dataset[[\"z\"]]   X <- dataset[[\"X\"]]  ite_pred <- ... # personalized ite estimation cre_results <- cre(y, z, X, ite = ite_pred) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = c(\"x1\",\"x2\",\"x3\",\"x4\"),                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.8,                      pfer = 1,                      B = 10,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"environment-setup","dir":"Articles","previous_headings":"","what":"Environment Setup","title":"Contribution","text":"Please follow steps get copy CRE Github account. Navigate CRE Github repository, top right corner, click Fork button. add clone project Github account. Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): already SSH key, need generate one. Read . Now, can modify codebase track modification. good idea create new branch work codebase. Read following instructions git branching.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"git-branching-model","dir":"Articles","previous_headings":"","what":"Git Branching Model","title":"Contribution","text":"Although, personal repository, can pick branch name, however, order keep consistency also understand working , following convention strongly recommended. project, follow convention proposed Vincent Driessen successful Git branching model post.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"where-to-submit-pull-requests","dir":"Articles","previous_headings":"","what":"Where to submit pull requests?","title":"Contribution","text":"pull requests submitted base repository: fasrc/CRE base: develop branch.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"pull-request-checklist","dir":"Articles","previous_headings":"","what":"Pull request checklist","title":"Contribution","text":"Please run devtools::document(), devtools::load_all() final modifications. Make sure modified code passes checks tests (can run devtools::check() RStudio) PR pass CI reviews can merge . Add line(s) modification NEWS.md file. adding new features, please make sure appropriate documentation added updated. Please clean white spaces. Read .","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"reporting-bugs","dir":"Articles","previous_headings":"","what":"Reporting bugs","title":"Contribution","text":"Please report potential bugs creating new issue sending us email. Please include following information bug report: brief description , expected happen, happened. OS using whether using personal computer HPC cluster. version package installed.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"style-guide","dir":"Articles","previous_headings":"","what":"Style Guide","title":"Contribution","text":"project, follow tidyverse style guide.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"names","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Names","title":"Contribution","text":"File names snake_case ends .R (e.g., create_matching.R) variable names small letter separate _ need (e.g., delta_n) Function names follow snake_case style (e.g., generate_data) Function names follow verb+output convention (e.g., compute_resid)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"spaces-and-indentation","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Spaces and Indentation","title":"Contribution","text":"Indentations two spaces (use tab) Place space around binary operators (e.g., x + y) Place space comma Place space # commenting avoid multiple ### put space opening closing parenthesis Place space () used , , .","code":"#Acceptable: z <- x + y #Not recommended: z<-x+y # (no space) z<- x+y z<-x +y #Acceptable: a <- matrix(c(1:100), nrow = 5) #Not recommended: a <- matrix(c(1:100),nrow = 5) # (no space after comma) a <- matrix( c(1:100), nrow = 5 ) # (extra space after and before parentheses) a<-matrix(c(1:100), nrow = 5) # (no space around unary operator <- ) #Acceptable: # This is a comment #Not recommended: #This is a comment #    This is a comment (more than one space after #) ## This is a comment (multiple #) ###    This is a comment (multiple # and more than one space) #Acceptable: x <- (z + y) #Not recommended: x <- ( z + y ) # (unnecessary space) x <- (z + y ) x <- ( z + y) #Acceptible if (x > 2) {   print(x) } # Not recommended if(x > 2){   print(x) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"other-notes","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Other notes","title":"Contribution","text":"Maximum line length 80 character Use explicit returns Use explicit tags documentation (e.g., @title, @description, …)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"getting-the-code","dir":"Articles","previous_headings":"","what":"Getting the code","title":"Testing the CRE Package","text":"test package, please install package system (R (>= 3.5.0)). can install package following one approaches: Directly GitHub CRAN (recommended) Source Forked Repository (recommended)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-directly-from-github","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package directly from Github","title":"Testing the CRE Package","text":"project, follow successful Git Branching Model. result, develop branch updated branch developers. Use devtools::install_github install package. specify ref, install master (main) branch. Try ?CRE. open package description page help tab (assuming using RStudio).","code":"library(devtools) try(detach(\"package:CRE\", unload = TRUE), silent = TRUE) # if already you have the package, detach and unload it, to have a new install.  install_github(\"NSAPH-Software/CRE\", ref=\"develop\") library(CRE)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-cran","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from CRAN","title":"Testing the CRE Package","text":"Installing package CRAN developing purposes recommended. probably, version CRAN latest version. [Complete section submitting package CRAN]","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-the-source","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from the source","title":"Testing the CRE Package","text":"order install package source, need download source code computer install source. steps: Go package Github repository drop-menu change branch develop. click Code tab click Download Zip tab. Open one files using RStudio, change project directory project directory (Session > Set Working Directory > Project Directory). Load devtools library load CRE.","code":"library(devtools) load_all() ?CRE"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"forking-the-package","dir":"Articles","previous_headings":"Getting the code","what":"Forking the package","title":"Testing the CRE Package","text":"Forking package Github account best option planning installing, testing, modifying, contributing project. Go package Github repository click Fork button top right corner. forking package, Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): Now, can modify codebase track modification. Navigate package folder Install package following Installing package source steps. good idea create new branch work codebase. Read successful Git Branching Model branching convention.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"testing-the-package","dir":"Articles","previous_headings":"","what":"Testing the Package","title":"Testing the CRE Package","text":"Run following command test package.","code":"library(CRE)  # Generate sample data set.seed(1358) dataset <- generate_cre_dataset(n = 1000,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method = \"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\",                       offset = NULL)  hyper_params <- list(intervention_vars = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.01,                      t_corr = 1,                      t_pvalue = 0.05,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 10,                      subsample = 0.5)  # linreg CATE estimation with aipw ITE estimation cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            FASRC Daniela Maria Garcia. Author. Riccardo Cadei. Author. Kwonsang Lee. Author. Falco Joannes Bargagli Stoffi. Author.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Khoshnevis N, Garcia D, Cadei R, Lee K, Bargagli Stoffi F (2023). CRE: Interpretable Discovery Inference Heterogeneous Treatment Effects. R package version 0.2.5.9000, https://github.com/NSAPH-Software/CRE.","code":"@Manual{,   title = {CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},   author = {Naeem Khoshnevis and Daniela Maria Garcia and Riccardo Cadei and Kwonsang Lee and Falco Joannes {Bargagli Stoffi}},   year = {2023},   note = {R package version 0.2.5.9000},   url = {https://github.com/NSAPH-Software/CRE}, }"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"Identifying subgroups study population treatment exposure notably larger smaller effect outcome compared population average crucial social health sciences. estimating conditional average treatment effect (CATE) given pre-specified set covariates common approach, allows estimating causal effects subgroups specified priori researchers. package implements recently developed Causal Rule Ensemble (CRE) algorithm, flexible stable method interpretable discovery estimation heterogeneous treatment effects terms decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"package-details","dir":"","previous_headings":"","what":"Package Details","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"cre() function executes entire CRE algorithm utilizing data set input parameters user provides. divides study data discovery inference subsamples, generating filtering decision rules discovery subsample explain heterogeneity treatment effects. cre() uses inference subsample estimate linear decomposition Conditional Average Treatment Effect (CATE) terms just discovered decision rules, normal equations model ITE estimates. steps, cre() agnostic concerning method used ITE estimation. estimate Individual Treatment Effects (ITE), users can choose various methodologies vary terms speed accuracy. Several ITE estimators internally implemented (.e. AIPW, Causal Forest, Bayesian Causal Forest, Causal BART, S-Learner, T-Learner, X-Learner), customized ITE estimation can also provided input. results collected S3 object containing: number decision rules extracted step, CATE decomposition estimates corresponding uncertainty quantification, list selected parameters, predicted ITEs.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"applications","dir":"","previous_headings":"","what":"Applications","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"CRE package can used diverse theoretical practical applications long working binary exposure treatment level. example, can used education determine teaching methods effective specific student subgroups. marketing, can identify effective strategies different consumer subgroups. healthcare, can used identify patient subgroups benefit particular treatment intervention. can also used studies personalized medicine subgroup patients responds differently provided medicine. public health, CRE method applied discover subgroups vulnerable (resilient) causal effects long-term exposure air pollution mortality.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The CRE package — CRE-package","title":"The CRE package — CRE-package","text":"health social sciences, critically important identify subgroups study population treatment notable heterogeneity causal effects respect average treatment effect. Data-driven discovery heterogeneous treatment effects (HTE) via decision tree methods proposed task. Despite high interpretability, single-tree discovery HTE tends highly unstable find oversimplified representation treatment heterogeneity. accommodate shortcomings, propose Causal Rule Ensemble (CRE), new method discover heterogeneous subgroups ensemble--trees approach. CRE following features: provides interpretable representation HTE; 2) allows extensive exploration complex heterogeneity patterns; 3) guarantees high stability discovery. discovered subgroups defined terms interpretable decision rules, develop general two-stage approach subgroup-specific conditional causal effects estimation, providing theoretical guarantees.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The CRE package — CRE-package","text":"Bargagli-Stoffi, F. J., Cadei, R., Lee, K. Dominici, F. (2023). Causal rule ensemble: Interpretable Discovery Inference Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The CRE package — CRE-package","text":"Naeem Khoshnevis Daniela Maria Garcia Riccardo Cadei Kwonsang Lee Falco Joannes Bargagli Stoffi","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Sensitivity Analysis — analyze_sensitivity","title":"Sensitivity Analysis — analyze_sensitivity","text":"Method analyzing sensitivity estimates causal rule-specific treatment effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sensitivity Analysis — analyze_sensitivity","text":"","code":"analyze_sensitivity(ite_std, rules_matrix_std)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sensitivity Analysis — analyze_sensitivity","text":"ite_std standardized ITE rules_matrix_std standardized causal rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sensitivity Analysis — analyze_sensitivity","text":"list containing results sensitivity analysis","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cre object — autoplot.cre","title":"A helper function for cre object — autoplot.cre","text":"helper function plot cre object using ggplot2 package.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cre object — autoplot.cre","text":"","code":"# S3 method for cre autoplot(object, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cre object — autoplot.cre","text":"object cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cre object — autoplot.cre","text":"Returns ggplot object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_hyper_params","title":"Check input parameters — check_hyper_params","text":"Checks consistency input (hyper) parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_hyper_params","text":"","code":"check_hyper_params(X_names, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_hyper_params","text":"X_names observed covariates names. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_hyper_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_input","title":"Check input parameters — check_input","text":"Checks input parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_input","text":"","code":"check_input(y, z, X, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_input","text":"y observed response vector. z treatment vector. X covariate matrix. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_input","text":"list params might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input data — check_input_data","title":"Check input data — check_input_data","text":"Conducts sanity checks input data.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input data — check_input_data","text":"","code":"check_input_data(y, z, X, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input data — check_input_data","text":"y observed response vector. z treatment vector. X features matrix. ite estimated ITE vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input data — check_input_data","text":"number data samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check method-related parameters — check_method_params","title":"Check method-related parameters — check_method_params","text":"Checks method-related parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check method-related parameters — check_method_params","text":"","code":"check_method_params(y, ite, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check method-related parameters — check_method_params","text":"y observed response vector. ite estimated ITE vector. params list parameters required run method functions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check method-related parameters — check_method_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Causal rule ensemble — cre","title":"Causal rule ensemble — cre","text":"Performs Causal Rule Ensemble data set response variable, treatment variable, various features.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Causal rule ensemble — cre","text":"","code":"cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Causal rule ensemble — cre","text":"y observed response vector. z treatment vector. X covariate matrix (data frame). provided numerical values. method_params list parameters define models used, including: Parameters Honest Splitting ratio_dis: ratio data delegated rules discovery (default: 0.5). Parameters Discovery Inference ite_method: method ITE (pseudo-outcome) estimation (default: \"aipw\", options: \"aipw\" Augmented Inverse Probability Weighting, \"cf\" Causal Forest, \"bart\" Causal Bayesian Additive Regression Trees, \"slearner\" S-Learner, \"tlearner\" T-Learner, \"xlearner\" X-Learner, \"tpoisson\" T-Learner Poisson regression). learner_ps: model propensity score estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"bart\", \"cf\" ITE estimators). learner_y: model outcome estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"slearner\", \"tlearner\" \"xlearner\" ITE estimators). hyper_params list hyper parameters fine-tune method, including: General hyper parameters intervention_vars: Array intervention-able covariates names used Rules Generation. Empty null array means covariates considered intervention-able (default: NULL). ntrees: number decision trees random forest (default: 20). node_size: Minimum size trees' terminal nodes (default: 20). max_rules: Maximum number generated candidates rules (default: 50). max_depth: Maximum rules length (default: 3). t_decay: decay threshold rules pruning. Higher values carry aggressive pruning (default: 0.025). t_ext: threshold truncate generic specific (extreme) rules (default: 0.01, range: [0, 0.5)). t_corr: threshold define correlated rules (default: 1, range: [0,+inf)). stability_selection: Method stability selection selecting rules. \"vanilla\" stability selection, \"error_control\" stability selection error control  \"\" stability selection (default: \"vanilla\"). B: Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). subsample: Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). Method specific hyper parameters offset: Name covariate use offset (.e., \"x1\") T-Poisson ITE estimation. Use NULL offset used (default: NULL). cutoff:  Threshold (percentage) defining minimum cutoff value stability scores Stability Selection (default: 0.9). pfer: Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). ite estimated ITE vector. given ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Causal rule ensemble — cre","text":"S3 object composed : M number Decision Rules extracted step, CATE data.frame Conditional Average Treatment Effect decomposition estimates corresponding uncertainty quantification, method_params list method parameters, hyper_params list hyper parameters, rules list rules (implicit form) decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Causal rule ensemble — cre","text":"intervention_vars provided, important note individual treatment effect still computed using covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Causal rule ensemble — cre","text":"","code":"# \\donttest{ set.seed(123) dataset <- generate_cre_dataset(n = 400,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = NULL,                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 20,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) #> Loading required package: nnls #> Registered S3 method overwritten by 'randomForest': #>   method      from #>   plot.margin RRF  # }"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":null,"dir":"Reference","previous_headings":"","what":"The Causal Rule Ensemble Crossfit — cre_crossfit","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"Performs Causal Rule Ensemble twice dataset response variable, treatment variable, various features, swapping discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"cre_crossfit(   y,   z,   X,   ite_method_dis,   include_ps_dis = NA,   ps_method_dis = \"SL.xgboost\",   or_method_dis = NA,   ite_method_inf,   include_ps_inf = NA,   ps_method_inf = \"SL.xgboost\",   or_method_inf = NA,   ntrees_rf,   ntrees_gbm,   min_nodes,   max_nodes,   t,   q,   rules_method = NA,   include_offset = FALSE,   offset_name = NA,   cate_method = \"DRLearner\",   cate_SL_library = \"SL.xgboost\",   filter_cate = FALSE )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"y observed response vector z treatment vector X covariate matrix ite_method_dis method estimate discovery sample ITE include_ps_dis whether include propensity score estimate covariate discovery ITE estimation, considered BART, XBART, CF ps_method_dis estimation model propensity score discovery subsample or_method_dis estimation model outcome regressions estimate_ite_aipw discovery subsample ite_method_inf method estimate inference sample ITE include_ps_inf whether include propensity score estimate covariate inference ITE estimation, considered BART, XBART, CF ps_method_inf estimation model propensity score inference subsample or_method_inf estimation model outcome regressions estimate_ite_aipw inference subsample ntrees_rf number decision trees randomForest ntrees_gbm number decision trees gradient boosting min_nodes minimum size trees' terminal nodes max_nodes maximum size trees' terminal nodes t common support used generating causal rules matrix q selection threshold used selecting causal rules rules_method method selecting causal rules binary outcomes include_offset whether include offset estimating ITE, poisson offset_name name offset, included cate_method method estimate CATE values cate_SL_library library used cate_method set DRLearner filter_cate whether filter rules p-value <= 0.05","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"S3 object containing matrix averaged Conditional Average Treatment Effect estimates two CRE iterations","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  cre_cross_results <- cre_crossfit(y = dataset[[\"y\"]], z = dataset[[\"z\"]],                                   X = as.data.frame(dataset[[\"X\"]]),                                   ite_method_dis = \"bart\",                                   include_ps_dis = TRUE,                                   ite_method_inf = \"bart\",                                   include_ps_inf = TRUE,                                   ntrees_rf = 100, ntrees_gbm = 50,                                   min_nodes = 20, max_nodes = 5,                                   t = 0.025, q = 0.8) #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> Conducting Discovery Subsample Analysis 1 #> 411 rules (length<=15) were extracted from the first 100 trees. #> 150 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 1 #> Conducting Discovery Subsample Analysis 2 #> 493 rules (length<=15) were extracted from the first 100 trees. #> 100 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 2"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"Discard rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"","code":"discard_anomalous_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"Discard redundant correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"","code":"discard_correlated_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover causal decision rules — discover_causal_rules","title":"Discover causal decision rules — discover_causal_rules","text":"Discovers Causal Decision Rules randomization-based tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover causal decision rules — discover_causal_rules","text":"","code":"discover_causal_rules(   rules_matrix,   rules_list,   ite,   stability_selection,   cutoff,   pfer,   penalty_rl )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover causal decision rules — discover_causal_rules","text":"rules_matrix causal rules matrix. rules_list vector causal rules. ite estimated ITE. stability_selection Whether using stability selection select causal rules. cutoff Threshold (percentage) defining minimum cutoff value stability scores. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). penalty_rl Order penalty rules length LASSO Causal Rules Discovery (.e. 0: penalty, 1: rules_length, 2: rules_length^2)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover causal decision rules — discover_causal_rules","text":"List Causal Decision Rules discovered","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover rules — discover_rules","title":"Discover rules — discover_rules","text":"Discover minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover rules — discover_rules","text":"","code":"discover_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover rules — discover_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover rules — discover_rules","text":"minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Conditional Average Treatment Effect — estimate_cate","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"Estimates Conditional Average Treatment Effect (CATE) linearly modeling Individual Treatment Effect set rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"","code":"estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"rules_matrix rules matrix. rules_explicit list select rules terms covariate names. ite estimated ITEs. B number bootstrap samples uncertainty quantification estimation. subsample bootstrap ratio subsample uncertainty quantification estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"list 2 elements: summary: data frame summarizing CATE linear decomposition: Rule: rule name, Estimate: linear contribution CATE, CI_lower: lower bound 95% confidence interval estimate, CI_upper: upper bound 95% confidence interval estimate, P_Value: p-value (Z-test). model: linear model CATE-ATE estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"Estimates Individual Treatment Effect given response vector, treatment vector, covariate matrix, desired algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"","code":"estimate_ite(y, z, X, ite_method, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"y observed response vector. z observed treatment vector. X covariate matrix. ite_method method estimating Individual Treatment Effect. methods requires additional parameters. parameters mentioned indented blocks method definitions provided end parameters list. \"slearner\": S-Learner. - learner_y \"tlearner\": T-Learner. learner_y \"tpoisson\": T-Poisson. offset \"xlearner\": X-Learner. learner_y \"aipw\": Augmented Inverse Probability Weighting. learner_ps  learner_y \"bart\": Bayesian Additive Regression Trees. learner_ps \"cf\": Causal Forest. learner_ps ... Additional parameters passed different models.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"list ITE estimates.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"additional-parameters","dir":"Reference","previous_headings":"","what":"Additional parameters","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"learner_ps: estimation method propensity score. includes libraries SuperLearner package. learner_y: estimation model outcome. includes libraries SuperLearner package. offset: Name covariate use offset (.e. \"x1\") Poisson ITE Estimation. NULL offset used.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"Estimates Individual Treatment Effect using Augmented Inverse Probability Weighting given response vector, treatment vector, features matrix, estimation model propensity score estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"","code":"estimate_ite_aipw(y, z, X, learner_ps = \"SL.xgboost\", learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"y observed response vector. z treatment vector. X features matrix. learner_ps estimation model propensity score. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"Estimates Individual Treatment Effect using Bayesian Additive Regression Trees given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"","code":"estimate_ite_bart(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"y observed response vector. z treatment vector. X features matrix. learner_ps Method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"number samples number burn set default equal 500.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"Estimates Individual Treatment Effect using Bayesian Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"","code":"estimate_ite_bcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"y observed response vector. z treatment vector. X features matrix. ps_method method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"Estimates Individual Treatment Effect using Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"","code":"estimate_ite_cf(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"y observed response vector. z treatment vector. X features matrix. learner_ps method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"Estimates Individual Treatment Effect (ITE) using Inverse Propensity Weighting (IPW) given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"","code":"estimate_ite_ipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"Method estimating Individual Treatment Effect using Outcome Regression given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"estimate_ite_or(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"y observed response vector z treatment vector X features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  # Initialize parameters y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- as.data.frame(dataset[[\"X\"]])  ite_list <- estimate_ite_or(y, z, X) #> *****Into main of wbart #> *****Data: #> data:n,p,np: 388, 10, 612 #> y1,yn: -0.694294, 1.362455 #> x1,x[n*p]: 1.000000, 1.000000 #> xp1,xp[np*p]: 0.000000, 0.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.118817,3.000000,0.250422 #> *****sigma: 1.133838 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000 #> *****Into main of wbart #> *****Data: #> data:n,p,np: 612, 10, 388 #> y1,yn: -0.485955, 0.857738 #> x1,x[n*p]: 0.000000, 0.000000 #> xp1,xp[np*p]: 1.000000, 1.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.139249,3.000000,0.168737 #> *****sigma: 0.930723 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"Estimates Individual Treatment Effect (ITE) using Outcome Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"","code":"estimate_ite_oreg(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"y observed response vector. z treatment vector. X features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"Estimates Individual Treatment Effect using Poisson Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"","code":"estimate_ite_poisson(y, z, X, X_names, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"y observed response vector. z treatment vector. X features matrix. X_names vector includes names covariates. offset name covariate use offset (.e. 'x1') model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"Estimates Individual Treatment Effect using Stabilized Inverse Propensity Weighting given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"","code":"estimate_ite_sipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"Estimates Individual Treatment Effect using S-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"","code":"estimate_ite_slearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"Estimates Individual Treatment Effect using T-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"","code":"estimate_ite_tlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"Estimates Individual Treatment Effect using Poisson regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"","code":"estimate_ite_tpoisson(y, z, X, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"y observed response vector. z treatment vector. X features matrix. offset name covariate use offset (.e. “x1”) model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"","code":"estimate_ite_xbart(y, z, X, include_ps, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"y observed response vector z treatment vector X features matrix include_ps whether include propensity score estimate covariate ITE estimation ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Causal Forest given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"","code":"estimate_ite_xbcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"y observed response vector z treatment vector X features matrix ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"Estimates Individual Treatment Effect using X-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"","code":"estimate_ite_xlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the propensity score — estimate_ps","title":"Estimate the propensity score — estimate_ps","text":"Estimates Propensity Score given treatment vector features data frame.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the propensity score — estimate_ps","text":"","code":"estimate_ps(z, X, ps_method = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the propensity score — estimate_ps","text":"z treatment vector. X features data frame. ps_method estimation model propensity score (default: SL.xgboost).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the propensity score — estimate_ps","text":"vector propensity score estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":null,"dir":"Reference","previous_headings":"","what":"Discovery (performance) evaluation — evaluate","title":"Discovery (performance) evaluation — evaluate","text":"Computes different metrics discovery evaluation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discovery (performance) evaluation — evaluate","text":"","code":"evaluate(ground_truth, prediction)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discovery (performance) evaluation — evaluate","text":"ground_truth List true (CDR/EM) values. prediction List predicted (CDR/EM) values.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discovery (performance) evaluation — evaluate","text":"Intersection union, precision, recall.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract effect modifiers — extract_effect_modifiers","title":"Extract effect modifiers — extract_effect_modifiers","text":"Extracts effect modifiers list (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract effect modifiers — extract_effect_modifiers","text":"","code":"extract_effect_modifiers(rules_list, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract effect modifiers — extract_effect_modifiers","text":"rules_list list (causal) decision rules. X_names list covariate names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract effect modifiers — extract_effect_modifiers","text":"list effect modifiers.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract (causal) decision rules — extract_rules","title":"Extract (causal) decision rules — extract_rules","text":"Extracts causal rules random forest gradient boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract (causal) decision rules — extract_rules","text":"","code":"extract_rules(treelist, X, max_depth, digits = 2)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract (causal) decision rules — extract_rules","text":"treelist list decision trees. X Features matrix. max_depth number top levels tree considered. digits Number digits rounding decision rules extract conditions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract (causal) decision rules — extract_rules","text":"vector (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter correlated rules — filter_correlated_rules","title":"Filter correlated rules — filter_correlated_rules","text":"Discards highly correlated rules (.e., \\(Cov(rule_1,rule_2) > t_{corr}\\)).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter correlated rules — filter_correlated_rules","text":"","code":"filter_correlated_rules(rules_matrix, rules_list, t_corr)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter correlated rules — filter_correlated_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_corr threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter correlated rules — filter_correlated_rules","text":"rules matrix without highly correlated rules (columns).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter extreme decision rules — filter_extreme_rules","title":"Filter extreme decision rules — filter_extreme_rules","text":"Discards rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter extreme decision rules — filter_extreme_rules","text":"","code":"filter_extreme_rules(rules_matrix, rules_list, t_ext)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_ext threshold [0, 0.5) range truncate generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules matrix without rare/common rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"Filters irrelevant decision rules. irrelevant rules interpreted error increase removing variable-value pair decision rules (see “Interpreting tree ensembles inTrees package” Houtao Deng, 2019).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"","code":"filter_irrelevant_rules(rules, X, ite, t_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"rules list rules. X features matrix. ite estimated ITE. t_decay decay threshold rules pruning.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"list “relevant” rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate causal rules — generate_causal_rules","title":"Generate causal rules — generate_causal_rules","text":"Generates causal decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate causal rules — generate_causal_rules","text":"","code":"generate_causal_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate causal rules — generate_causal_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate causal rules — generate_causal_rules","text":"vector causal rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate CRE synthetic data — generate_cre_dataset","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Generates synthetic data sets run simulation causal inference experiments composed outcome vector (y), treatment vector (z), covariates matrix (X), unobserved individual treatment effects vector (ite). arguments specify data set characteristic, including number individuals (n), number covariates (p), correlation within covariates (rho),  number decision rules (n_rules) decomposing Conditional Average Treatment Effect (CATE), treatment effect magnitude (effect_size), confounding mechanism (confounding), whether covariates outcomes binary continuous (binary_covariates, binary_outcome).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"generate_cre_dataset(   n = 1000,   rho = 0,   n_rules = 2,   p = 10,   effect_size = 2,   binary_covariates = TRUE,   binary_outcome = TRUE,   confounding = \"no\" )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate CRE synthetic data — generate_cre_dataset","text":"n integer number represents number observations. Non-integer values converted integer number. rho positive double number represents correlation within covariates (default: 0, range: [0,1)). n_rules number causal rules (default: 2, range: {1,2,3,4}). p number covariates (default: 10). effect_size treatment effect size magnitude (default: 2, range: \\(\\geq\\)0). binary_covariates Whether use binary continuous covariates (default: TRUE). binary_outcome Whether use binary continuous outcomes (default: TRUE). confounding continuous outcome, add confounding variables: \"lin\" linear confounding, \"nonlin\" non-linear confounding, \"\" confounding (default).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate CRE synthetic data — generate_cre_dataset","text":"list, representing generated synthetic data set, containing: y outcome vector, z treatment vector, X covariates matrix, ite individual treatment vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate CRE synthetic data — generate_cre_dataset","text":"covariates matrix generated specified correlation among individuals, covariate sampled either Bernoulli(0.5) binary, Gaussian(0,1) continuous. treatment vector sampled Bernoulli(\\(\\frac{1}{1+ \\exp(1-x_1+x_2-x_3)}\\)), enforcing treatment assignment probabilities function observed covariates. potential outcomes (\\(y(0)\\) \\(y(1)\\)) sampled Bernoulli binary, Gaussian (standard deviation equal 1) continuous. mean equal confounding term (null, linear non-linear always null binary outcome) plus 1-4 decision rules weighted treatment effect magnitude. two potential outcomes characterizes CATE (unobserved individual treatment effects vector) sum different additive contributions decision rules considered (plus intercept). final expression CATE depends treatment effect magnitude number decision rules considered. 4 decision rules : Rule 1: \\(1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x})\\) Rule 2: \\(1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})\\) Rule 3: \\(1\\{x_4 \\leq 0.5\\}(\\textbf{x})\\) Rule 4: \\(1\\{x_5 \\leq 0.5; x_7 > 0.5; x_8 \\leq 0.5\\}(\\textbf{x})\\) corresponding additive average treatment effect (AATE) equal : Rule 1: \\(-\\) effect_size, Rule 2: \\(+\\) effect_size, Rule 3: \\(- 0.5 \\cdot\\) effect_size, Rule 4: \\(+ 2 \\cdot\\) effect_size. example, setting effect_size=4 n_rules=2: $$\\text{CATE}(\\textbf{x}) = -4 \\cdot 1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x}) + 4 \\cdot 1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})$$ final outcome vector y finally computed combining potential outcomes according treatment assignment.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Set covariates domain (binary_covariates) outcome domain (binary_outcome) according experiment interest. Increase complexity heterogeneity discovery: decreasing sample size (n), adding correlation among covariates (rho), increasing number rules (n_rules), increasing number covariates (p), decreasing absolute value causal effect (effect_size), adding linear -linear confounders (confounding).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"set.seed(123) dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary_covariates = TRUE,                                 binary_outcome = TRUE, confounding = \"no\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules — generate_rules","title":"Generate rules — generate_rules","text":"Generates list rules characterizing heterogeneity Conditional Average Treatment Effect (CATE) tree-based methods (.e., random forest).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules — generate_rules","text":"","code":"generate_rules(X, ite, ntrees, node_size, max_rules, max_depth)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules — generate_rules","text":"X covariate matrix. ite vector estimated ITE. ntrees number decision trees random forest algorithm. node_size Minimum size trees' terminal nodes. max_rules Maximum number candidate decision rules. max_depth Maximum rules length.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules — generate_rules","text":"list rules (names).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules matrix — generate_rules_matrix","title":"Generate rules matrix — generate_rules_matrix","text":"Generates rules matrix feature covariate matrix vector rules. number rows rules_matrix equal number samples X, number columns equal number rules rules_list. element rules_matrix corresponds specific data sample rule. data sample satisfies rule, corresponding element rules_matrix set 1. Otherwise, element set 0.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules matrix — generate_rules_matrix","text":"","code":"generate_rules_matrix(X, rules_list)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules matrix — generate_rules_matrix","text":"X Features matrix. rules_list vector rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules matrix — generate_rules_matrix","text":"causal rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Logger settings — get_logger","title":"Get Logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":null,"dir":"Reference","previous_headings":"","what":"Honest splitting — honest_splitting","title":"Honest splitting — honest_splitting","text":"Splits data discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Honest splitting — honest_splitting","text":"","code":"honest_splitting(y, z, X, ratio_dis, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Honest splitting — honest_splitting","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis double number indicating ratio data delegated discovery sub-sample. ite vector estimated ITE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Honest splitting — honest_splitting","text":"list containing discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret rules — interpret_rules","title":"Interpret rules — interpret_rules","text":"Replaces column numbers rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret rules — interpret_rules","text":"","code":"interpret_rules(rules, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret rules — interpret_rules","text":"rules vector rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret rules — interpret_rules","text":"list explicit (human-readable) rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret select rules — interpret_select_rules","title":"Interpret select rules — interpret_select_rules","text":"Replaces column numbers select rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret select rules — interpret_select_rules","text":"","code":"interpret_select_rules(select_rules_dis, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret select rules — interpret_select_rules","text":"select_rules_dis vector select causal rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret select rules — interpret_select_rules","text":"vector select causal rules interpretable.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"","code":"lasso_rules_filter(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q Number (unique) selected rules per subsample stability selection. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cre class — plot.cre","title":"Extend generic plot functions for cre class — plot.cre","text":"wrapper function extend generic plot functions cre class.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cre class — plot.cre","text":"","code":"# S3 method for cre plot(x, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cre class — plot.cre","text":"x cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cre class — plot.cre","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict individual treatment effect via causal rule ensemble — predict.cre","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"Predicts individual treatment effect via causal rule ensemble algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"","code":"# S3 method for cre predict(object, X, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"object cre object running CRE function. X covariate matrix (data.frame) ... Additional arguments passed customize prediction.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"array estimated Individual Treatment Effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for the CRE object — print.cre","title":"Extend print function for the CRE object — print.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for the CRE object — print.cre","text":"","code":"# S3 method for cre print(x, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for the CRE object — print.cre","text":"x cre object running CRE function. verbose Set level results description details: 0 results summary, 1 results parameters summary, 2 results parameters rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for the CRE object — print.cre","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune (Causal) Decision Rules — prune_rules","title":"Prune (Causal) Decision Rules — prune_rules","text":"Method pruning (Causal) Decision Rules extracted Random Forest Gradient Boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune (Causal) Decision Rules — prune_rules","text":"","code":"prune_rules(rules, X, ite_std, max_decay, type_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune (Causal) Decision Rules — prune_rules","text":"rules list decision rules. X features matrix. ite_std standardized ITE. max_decay Decay Threshold pruning rules. type_decay Decay Type pruning rules (1: relative error; 2: error).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune (Causal) Decision Rules — prune_rules","text":"list (Causal) Decision Rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select Causal Rules — select_causal_rules","title":"Select Causal Rules — select_causal_rules","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select Causal Rules — select_causal_rules","text":"","code":"select_causal_rules(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select Causal Rules — select_causal_rules","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q selection threshold used selecting causal rules. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select Causal Rules — select_causal_rules","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select rules — select_rules","title":"Select rules — select_rules","text":"Given set rules, selects minimal set linearly decomposing Conditional Average Treatment Effect (CATE) LASSO (optionally Stability Selection).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select rules — select_rules","text":"","code":"select_rules(rules_matrix, rules, ite, stability_selection, cutoff, pfer, B)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select rules — select_rules","text":"rules_matrix rules matrix. rules list rules. ite estimated ITE. stability_selection Stability selection method. cutoff Threshold (percentage) defining minimum cutoff value stability scores. stability selection. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). stability selection. B Number bootstrap samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select rules — select_rules","text":"minimal set rules linearly decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Logger settings — set_logger","title":"Set Logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"CRE.log\", logger_level = \"INFO\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: CRE.log) logger_level log level. log level set, log levels also activated (implemented). Available levels include: TRACE: Provides verbose detailed logging, including steps taken achieve result, often used debugging. Activating TRACE also enable DEBUG, INFO, SUCCESS, WARN, ERROR, FATAL logs. DEBUG: Provides detailed logging flow application, used mostly developers understand potential issues. Activating DEBUG also enable INFO, SUCCESS, WARN, ERROR, FATAL logs. INFO (Default): Standard messages inform user normal operation system. Activating INFO also enable SUCCESS, WARN, ERROR, FATAL logs. SUCCESS: Messages indicating successful completion particular operation task. Activating SUCCESS also enable WARN, ERROR, FATAL logs. WARN: Warning messages events might cause problems future, yet errors. Activating WARN also enable ERROR FATAL logs. ERROR: Reports error due system may able achieve functionality, application halt. Activating ERROR also enable FATAL logs. FATAL: Reports severe error events presumably lead application abort.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set Logger settings — set_logger","text":"Log levels specified developers initial implementation. Future developers contributors can leverage log levels better capture document application's processes events.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Logger settings — set_logger","text":"","code":"set_logger(\"Debug\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Data — split_data","title":"Split Data — split_data","text":"Splits data discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Data — split_data","text":"","code":"split_data(y, z, X, ratio_dis)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Data — split_data","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis ratio data delegated discovery subsample","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Data — split_data","text":"list containing discovery inference subsamples","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardize Rules Matrix — standardize_rules_matrix","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardize (.e. mean=0 stdev=1) rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"","code":"standardize_rules_matrix(rules_matrix)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"rules_matrix rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardized rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of CRE object — summary.cre","title":"Print summary of CRE object — summary.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of CRE object — summary.cre","text":"","code":"# S3 method for cre summary(object, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of CRE object — summary.cre","text":"object cre object running CRE function. verbose Set level results description details: results summary 0, results+parameters summary 1, results+parameters+rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of CRE object — summary.cre","text":"summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":null,"dir":"Reference","previous_headings":"","what":"Take 1 — take1","title":"Take 1 — take1","text":"Generate list indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take 1 — take1","text":"","code":"take1(len)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take 1 — take1","text":"len length value","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take 1 — take1","text":"vector indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take 1 — take1","text":"","code":"ind <- CRE:::take1(10)"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-5","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.5 (2023-12-6)","text":"Add (vanilla) Stability Selection (without Error Control). max_rules hyper parameters max rules filtering. Uncertainty Quantification estimation bootstrapping. B hyper-parameter, subsample hyper-parameter. rules(implicit form) cre() function return. predict() function ITE estimation via CRE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-5","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.5 (2023-12-6)","text":"Type stability_selection binary -> string (‘’,‘vanilla’,‘error_control’). Unify ntrees_gbm hyper-parameter ntrees_gbm hyper-parameter ntrees hyper-parameter. rules generation retrieve decision rules also internal nodes, just terminal nodes. ite_method_dis, ite_method_inf method-parameter -> ite_method. ps_method_dis, ps_method_inf method-parameter -> learner_ps. oreg_method_dis, oreg_method_inf method-parameter -> learner_y.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-5","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.5 (2023-12-6)","text":"max_nodes hyper-parameter. Remove rules generation Generalized Boosted Regression. replace hyper-parameter. penalty_rl hyper-parameter. t_pvalue hyper-parameter. ite_pred cre() function return.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.5 (2023-12-6)","text":"Error saving covariates name CRE result using intervention_vars.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-024-2023-6-14","dir":"Changelog","previous_headings":"","what":"CRE 0.2.4 (2023-6-14)","title":"CRE 0.2.4 (2023-6-14)","text":"CRAN release: 2023-06-14","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-4","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.4 (2023-6-14)","text":"Method paper description updated.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-023-2023-4-27","dir":"Changelog","previous_headings":"","what":"CRE 0.2.3 (2023-4-27)","title":"CRE 0.2.3 (2023-4-27)","text":"CRAN release: 2023-04-27","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-3","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.3 (2023-4-27)","text":"Bayesian Causal Forest (bcf) ITE estimator supported.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-022-2023-4-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.2 (2023-4-17)","title":"CRE 0.2.2 (2023-4-17)","text":"CRAN release: 2023-04-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-2","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.2 (2023-4-17)","text":"Fixed failing unit tests specific operating systems.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-021-2023-3-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.1 (2023-3-17)","title":"CRE 0.2.1 (2023-3-17)","text":"CRAN release: 2023-03-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.1 (2023-3-17)","text":"Replace BATE ATE CATE Linear Decomposition. Update plot() function (remove ATE, old BATE, explicit AATEs).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.1 (2023-3-17)","text":"Code Conduct.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.1 (2023-3-17)","text":"Causal Tree benchmark functional tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.1 (2023-3-17)","text":"Rank-Deficient Rule Matrix Issue (redundant rules). Intervention Variables Filtering (ordered filtering).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-020-2023-1-19","dir":"Changelog","previous_headings":"","what":"CRE 0.2.0 (2023-1-19)","title":"CRE 0.2.0 (2023-1-19)","text":"CRAN release: 2023-01-19","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.0 (2023-1-19)","text":"offset method-parameter -> hyper-parameter estimate_ite_poisson function -> estimate_ite_tpoisson max_dacay hyper-parameter -> t_decay. interpret_select_rules function -> interpret_rules. generate_causal_rules function -> discover_rules. discover_causal_rules function ->select_rules. offset_name method parameter -> offset. Hyper method parameters required arguments cre. cre object: added parameters ite estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.0 (2023-1-19)","text":"Synthetic data set 1 3 rules (generate_cre_dataset). S-Learner (slearner) method ITE estimation. T-Learner (tlearner) method ITE estimation. X-Learner (xlearner) method ITE estimation. Rules Selection description summary.cre. verbose parameter summary.cre. ite, additional cre input parameter use personalized ite estimations. Default values hyper parameters. Default values method parameters. Simulation experiments estimation (estimation.R). Simulation experiments discovery (discovery.R). extract_effect_modifiers function (utility performance evaluation). evaluate function discovery evaluation. confounding parameter generate_cre_dataset set confounding type. ite_pred model CRE results. binary_covariates parameter generate_cre_dataset set covariates domain.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.0 (2023-1-19)","text":"include_ps_inf method-parameter. include_ps_dis method-parameter. oreg method ITE estimation. ipw method ITE estimation. sipw method ITE estimation. ITE standard deviation estimation. type_decay hyper-parameter. Keep linreg CATE estimation (remove cate_method cate_SL_library parameters). method_params hyper_params additional parameters summary.cre. ite standardization Rules Generation. random_state parameter. include_offset method parameter.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.0 (2023-1-19)","text":"Rules Generation Issue (set rules length fix bootstrapping).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-011-2022-10-18","dir":"Changelog","previous_headings":"","what":"CRE 0.1.1 (2022-10-18)","title":"CRE 0.1.1 (2022-10-18)","text":"CRAN release: 2022-10-22","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.1 (2022-10-18)","text":"binary parameter generate_cre_dataset -> binary_outcome . filter_cate hyper-parameter -> t_pvalue. t_anom hyper-parameter -> t_ext. effect_modifier hyper-parameter -> intervention_vars. lasso_rules_filter function -> discover_causal_rules. split_data function -> honest_splitting. prune_rules function -> `filter_irrelevant_rules. discard_correlated_rules function -> filter_correlated_rules. discard_anomalous_rules function -> filter_extreme_rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.1 (2022-10-18)","text":"Weighted LASSO Causal Rules Discovery (penalty_rl hyper-parameter).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-010-2022-10-17","dir":"Changelog","previous_headings":"","what":"CRE 0.1.0 (2022-10-17)","title":"CRE 0.1.0 (2022-10-17)","text":"CRAN release: 2022-10-18","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.0 (2022-10-17)","text":"Update examples tests functions. q hyper-parameter -> cutoff. pfer_val hyper-parameter -> pfer. select_causal_rules function -> lasso_rules_filter. t hyper-parameter -> t_anom. Separate standardization, remove filtering generate_rules_matrix function. summary.cre function describe results. min_nodes hyper-parameter -> node_size (randomForest convention). cre returns S3 object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.0 (2022-10-17)","text":"Examples tests functions. prune_rules function discard un-predictive rules. discard_anomalous_rules function discard anomalous rules (see t_corr hyper-parameter.). discard_correlated_rules function discard correlated rules (see t_anom hyper-parameter). effect_modifiers parameter generate_rules function covariates filtering. generate_causal_rules function. Helper function SuperLearner package propensity score estimation estimate_ite_xyz. Five methods CATE estimation (poisson, DRLearner, bart-baggr, cf-means, linreg) estimate_cate function. (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) method-parameters complement SuperLearner package. cate_method method-parameter select CATE estimation method. filter_cate method-parameter estimation filtering. p parameter (generate_cre_dataset function) set number covariates. replace parameter (generate_rules function) allow bootstrapping. cre.print generic function print cre S3 object results. cre.summary generic functions summarize cre S3 object Results. check_input function isolate input checks. estimate_ite_aipw function augmented inverse propensity weighting. plot.cre generic function plot cre S3 object results. test-cre_functional.R test functionality package. stability_selection function causal rules selection.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-1-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.1.0 (2022-10-17)","text":"estimate_ite_blp function. take1() function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.1.0 (2022-10-17)","text":"Undesired ‘’ Decision Rule Issue. Causal Rule Selected Issue.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.0.1 (2021-10-20)","text":"estimate_cate include two methods estimating CATE values. cre added initial checks binary outcome whether include propensity score ITE estimation. estimate_ite_xyz conduct propensity score estimation using helper function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.0.1 (2021-10-20)","text":"Example generate_cre_dataset. set_logger get_logger. check_input_data function. generate_cre_dataset function generate synthetic data testing package. test-generate_cre_dataset function test. estimate_ps function estimate propensity score. estimate_ite_xbart function generate ITE estimates using accelerated BART. estimate_ite_xbcf function generate ITE estimates using accelerated BCF. analyze_sensitivity function conduct sensitivity analysis unmeasured confounding. cre function perform entire Causal Rule Ensemble method. estimate_cate function generate CATE estimates ITE estimates select rules. estimate_ite function generate ITE estimates using user-specified method (calls estimate_ite_xyz functions). estimate_ite_bart function generate ITE estimates using BART. estimate_ite_bcf function generate ITE estimates using Bayesian Causal Forests. estimate_ite_cf function generate ITE estimates using Causal Forests. estimate_ite_ipw function generate ITE estimates using IPW. estimate_ite_or function generate ITE estimates using Outcome Regression. estimate_ite_sipw function generate ITE estimates using SIPW. extract_rules function extract list causal rules randomForest GBM models. generate_rules function generate causal rule models using randomForest GBM methods. generate_rules_matrix function convert list causal rules matrix. select_causal_rules function apply penalized regression causal rules. select important ones. split_data function split input data discovery inference subsamples. take1 function create subsample indices.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-0-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.0.1 (2021-10-20)","text":"seed argument generate_cre_datase function.","code":""}]

From 7ac6029aa2211bbb4eae952b278b8bdf582f8971 Mon Sep 17 00:00:00 2001
From: naeemkh <khoshnevis.naeem@gmail.com>
Date: Fri, 15 Dec 2023 13:44:44 -0500
Subject: [PATCH 2/5] add joss badge

---
 README.md            | 32 +++++++++++++++++++++++++++-----
 docs/index.html      |  1 +
 docs/news/index.html |  2 +-
 docs/pkgdown.yml     |  2 +-
 docs/search.json     |  2 +-
 index.md             |  1 +
 6 files changed, 32 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index a86b5ade..c9c8004c 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,32 @@
-| Resource    |  GitHub Actions      |  Code Coverage  |
-| ----------  | -------------------- | --------------- |
-| Platforms   | Windows, macOS, Linux|    codecov      |
-| R CMD check | [![R-CMD-check](https://github.com/nsaph-software/CRE/workflows/R-CMD-check/badge.svg)](https://github.com/nsaph-software/CRE/actions) | [![codecov](https://codecov.io/gh/NSAPH-Software/CRE/branch/develop/graph/badge.svg?token=UMSVOYRKGA)](https://app.codecov.io/gh/NSAPH-Software/CRE)|
+# CRE
+
+<p align="center">
+  <img src="man/figures/png/CRE_logo.png" height="100" alt="Cover Image"/>
+</p>
+
+
+<div align="center">
+    <a href="https://CRAN.R-project.org/package=CRE">
+        <img src="http://www.r-pkg.org/badges/version-last-release/CRE" alt="CRAN Package Version">
+    </a>
+    <a href="https://joss.theoj.org/papers/86a406543801a395248821c08c7ec03d">
+        <img src="https://joss.theoj.org/papers/86a406543801a395248821c08c7ec03d/status.svg" alt="JOSS Status">
+    </a>
+    <a href="https://github.com/nsaph-software/CRE/actions">
+        <img src="https://github.com/nsaph-software/CRE/workflows/R-CMD-check/badge.svg" alt="R-CMD-check Status">
+    </a>
+    <a href="https://app.codecov.io/gh/NSAPH-Software/CRE">
+        <img src="https://codecov.io/gh/NSAPH-Software/CRE/branch/develop/graph/badge.svg?token=UMSVOYRKGA" alt="Codecov">
+    </a>
+    <a href="http://www.r-pkg.org/pkg/cre">
+        <img src="https://cranlogs.r-pkg.org/badges/grand-total/CRE" alt="CRAN RStudio Mirror Downloads">
+    </a>
+</div>
+
+
+
 
 
-# CRE
 #### Interpretable Discovery and Inference of Heterogeneous Treatment Effects
 
 In health and social sciences, it is critically important to identify subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect (ATE). The bulk of heterogeneous treatment effect (HTE) literature focuses on two major tasks: (i) estimating HTEs by examining the conditional average treatment effect (CATE); (ii) discovering subgroups of a population characterized by HTE. 
diff --git a/docs/index.html b/docs/index.html
index f5bcee73..a1877ec1 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -158,6 +158,7 @@ <h2 data-toc-skip>Developers</h2>
 <h2 data-toc-skip>Dev status</h2>
 <ul class="list-unstyled">
 <li><a href="https://CRAN.R-project.org/package=CRE" class="external-link"><img src="http://www.r-pkg.org/badges/version-last-release/CRE"></a></li>
+<li><a href="https://joss.theoj.org/papers/86a406543801a395248821c08c7ec03d" class="external-link"><img src="https://joss.theoj.org/papers/86a406543801a395248821c08c7ec03d/status.svg" alt="status"></a></li>
 <li><a href="https://github.com/fasrc/CRE/actions" class="external-link"><img src="https://github.com/fasrc/CRE/workflows/R-CMD-check/badge.svg" alt="R-CMD-check"></a></li>
 <li><a href="https://codecov.io/gh/NSAPH-Software/CRE" class="external-link"><img src="https://codecov.io/gh/NSAPH-Software/CRE/branch/develop/graph/badge.svg?token=UMSVOYRKGA" alt="codecov"></a></li>
 <li><a href="http://www.r-pkg.org/pkg/cre" class="external-link"><img src="https://cranlogs.r-pkg.org/badges/grand-total/CRE" alt="CRAN RStudio mirror downloads"></a></li>
diff --git a/docs/news/index.html b/docs/news/index.html
index 70560cb3..24f77255 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -58,7 +58,7 @@ <h6 class="dropdown-header" data-toc-skip>Developers</h6>
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="0.2.5" id="cre-025-2023-12-6">CRE 0.2.5 (2023-12-6)<a class="anchor" aria-label="anchor" href="#cre-025-2023-12-6"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.2.5" id="cre-025-2023-12-6">CRE 0.2.5 (2023-12-6)<a class="anchor" aria-label="anchor" href="#cre-025-2023-12-6"></a></h2><p class="text-muted">CRAN release: 2023-12-06</p>
 <div class="section level3">
 <h3 id="added-0-2-5">Added<a class="anchor" aria-label="anchor" href="#added-0-2-5"></a></h3>
 <ul><li>Add (vanilla) Stability Selection (without Error Control).</li>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index eb906830..6f39ed39 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   CRE: CRE.html
   Contribution: Contribution.html
   Testing-the-Package: Testing-the-Package.html
-last_built: 2023-12-06T20:56Z
+last_built: 2023-12-15T18:43Z
 urls:
   reference: https://github.com/NSAPH-Software/CRE/reference
   article: https://github.com/NSAPH-Software/CRE/articles
diff --git a/docs/search.json b/docs/search.json
index 371c690b..9e3ab17a 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement nkhoshnevis@g.harvard.edu. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"CRE","text":"Installing CRAN. Installing latest developing version. Import.","code":"install.packages(\"CRE\") library(devtools) install_github(\"NSAPH-Software/CRE\", ref = \"develop\") library(\"CRE\")"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"arguments","dir":"Articles","previous_headings":"","what":"Arguments","title":"CRE","text":"Data (required) y observed response/outcome vector (binary continuous). z treatment/exposure/policy vector (binary). X covariate matrix (binary continuous). Parameters (required) method_parameters list parameters define models used, including: - ratio_dis ratio data delegated discovery sub-sample (default: 0.5). - ite_method method estimate individual treatment effect (default: “aipw”) [1]. - learner_ps (SuperLearner) model propensity score estimation (default: “SL.xgboost”, used “aipw”,“bart”,“cf” ITE estimators). - learner_y (SuperLearner) model outcome estimation (default: “SL.xgboost”, used “aipw”,“slearner”,“tlearner” “xlearner” ITE estimators). hyper_params list hyper parameters fine tune method, including: - intervention_vars Intervention-able variables used Rules Generation (default: NULL). - ntrees number decision trees random forest (default: 20). - node_size Minimum size trees’ terminal nodes (default: 20). - max_rules Maximum number candidate decision rules (default: 50). - max_depth Maximum rules length (default: 3). - t_decay decay threshold rules pruning (default: 0.025). - t_ext threshold define generic specific (extreme) rules (default: 0.01). - t_corr threshold define correlated rules (default: 1). - stability_selection Method stability selection selecting rules. vanilla stability selection, error_control stability selection error control stability selection (default: vanilla). - B Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). - subsample Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). - offset Name covariate use offset (.e. “x1”) T-Poisson ITE Estimation. NULL used (default: NULL). - cutoff Threshold defining minimum cutoff value stability scores Stability Selection (default: 0.9). - pfer Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). Additional Estimates (required) ite estimated ITE vector. given, ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"options-for-the-ite-estimation","dir":"Articles","previous_headings":"Arguments > Notes","what":"Options for the ITE estimation","title":"CRE","text":"[1] Options ITE estimation follows: - S-Learner (slearner). - T-Learner (tlearner) - T-Poisson(tpoisson) - X-Learner (xlearner) - Augmented Inverse Probability Weighting (aipw) - Causal Forests (cf) - Causal Bayesian Additive Regression Trees (bart) estimates ITE provided ite additional argument, ITE estimations discovery inference skipped values estimates used instead. ITE estimator requires also outcome learner /propensity score learner SuperLearner package (.e., “SL.lm”, “SL.svm”). models simple classifiers/regressors. default XGBoost algorithm used steps.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"customized-wrapper-for-superlearner","dir":"Articles","previous_headings":"Arguments > Notes","what":"Customized wrapper for SuperLearner","title":"CRE","text":"One can create customized wrapper SuperLearner internal packages. following example providing number cores (e.g., 12) xgboost package shared memory system. use “m_xgboost”, instead “SL.xgboost”.","code":"m_xgboost <- function(nthread = 12, ...) {   SuperLearner::SL.xgboost(nthread = nthread, ...) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"CRE","text":"Example 1 (default parameters) Example 2 (personalized ite estimation) Example 3 (setting parameters) synthetic data sets can generated using generate_cre_dataset().","code":"set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  cre_results <- cre(y, z, X) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\")   y <- dataset[[\"y\"]]   z <- dataset[[\"z\"]]   X <- dataset[[\"X\"]]  ite_pred <- ... # personalized ite estimation cre_results <- cre(y, z, X, ite = ite_pred) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = c(\"x1\",\"x2\",\"x3\",\"x4\"),                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.8,                      pfer = 1,                      B = 10,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"environment-setup","dir":"Articles","previous_headings":"","what":"Environment Setup","title":"Contribution","text":"Please follow steps get copy CRE Github account. Navigate CRE Github repository, top right corner, click Fork button. add clone project Github account. Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): already SSH key, need generate one. Read . Now, can modify codebase track modification. good idea create new branch work codebase. Read following instructions git branching.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"git-branching-model","dir":"Articles","previous_headings":"","what":"Git Branching Model","title":"Contribution","text":"Although, personal repository, can pick branch name, however, order keep consistency also understand working , following convention strongly recommended. project, follow convention proposed Vincent Driessen successful Git branching model post.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"where-to-submit-pull-requests","dir":"Articles","previous_headings":"","what":"Where to submit pull requests?","title":"Contribution","text":"pull requests submitted base repository: fasrc/CRE base: develop branch.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"pull-request-checklist","dir":"Articles","previous_headings":"","what":"Pull request checklist","title":"Contribution","text":"Please run devtools::document(), devtools::load_all() final modifications. Make sure modified code passes checks tests (can run devtools::check() RStudio) PR pass CI reviews can merge . Add line(s) modification NEWS.md file. adding new features, please make sure appropriate documentation added updated. Please clean white spaces. Read .","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"reporting-bugs","dir":"Articles","previous_headings":"","what":"Reporting bugs","title":"Contribution","text":"Please report potential bugs creating new issue sending us email. Please include following information bug report: brief description , expected happen, happened. OS using whether using personal computer HPC cluster. version package installed.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"style-guide","dir":"Articles","previous_headings":"","what":"Style Guide","title":"Contribution","text":"project, follow tidyverse style guide.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"names","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Names","title":"Contribution","text":"File names snake_case ends .R (e.g., create_matching.R) variable names small letter separate _ need (e.g., delta_n) Function names follow snake_case style (e.g., generate_data) Function names follow verb+output convention (e.g., compute_resid)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"spaces-and-indentation","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Spaces and Indentation","title":"Contribution","text":"Indentations two spaces (use tab) Place space around binary operators (e.g., x + y) Place space comma Place space # commenting avoid multiple ### put space opening closing parenthesis Place space () used , , .","code":"#Acceptable: z <- x + y #Not recommended: z<-x+y # (no space) z<- x+y z<-x +y #Acceptable: a <- matrix(c(1:100), nrow = 5) #Not recommended: a <- matrix(c(1:100),nrow = 5) # (no space after comma) a <- matrix( c(1:100), nrow = 5 ) # (extra space after and before parentheses) a<-matrix(c(1:100), nrow = 5) # (no space around unary operator <- ) #Acceptable: # This is a comment #Not recommended: #This is a comment #    This is a comment (more than one space after #) ## This is a comment (multiple #) ###    This is a comment (multiple # and more than one space) #Acceptable: x <- (z + y) #Not recommended: x <- ( z + y ) # (unnecessary space) x <- (z + y ) x <- ( z + y) #Acceptible if (x > 2) {   print(x) } # Not recommended if(x > 2){   print(x) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"other-notes","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Other notes","title":"Contribution","text":"Maximum line length 80 character Use explicit returns Use explicit tags documentation (e.g., @title, @description, …)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"getting-the-code","dir":"Articles","previous_headings":"","what":"Getting the code","title":"Testing the CRE Package","text":"test package, please install package system (R (>= 3.5.0)). can install package following one approaches: Directly GitHub CRAN (recommended) Source Forked Repository (recommended)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-directly-from-github","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package directly from Github","title":"Testing the CRE Package","text":"project, follow successful Git Branching Model. result, develop branch updated branch developers. Use devtools::install_github install package. specify ref, install master (main) branch. Try ?CRE. open package description page help tab (assuming using RStudio).","code":"library(devtools) try(detach(\"package:CRE\", unload = TRUE), silent = TRUE) # if already you have the package, detach and unload it, to have a new install.  install_github(\"NSAPH-Software/CRE\", ref=\"develop\") library(CRE)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-cran","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from CRAN","title":"Testing the CRE Package","text":"Installing package CRAN developing purposes recommended. probably, version CRAN latest version. [Complete section submitting package CRAN]","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-the-source","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from the source","title":"Testing the CRE Package","text":"order install package source, need download source code computer install source. steps: Go package Github repository drop-menu change branch develop. click Code tab click Download Zip tab. Open one files using RStudio, change project directory project directory (Session > Set Working Directory > Project Directory). Load devtools library load CRE.","code":"library(devtools) load_all() ?CRE"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"forking-the-package","dir":"Articles","previous_headings":"Getting the code","what":"Forking the package","title":"Testing the CRE Package","text":"Forking package Github account best option planning installing, testing, modifying, contributing project. Go package Github repository click Fork button top right corner. forking package, Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): Now, can modify codebase track modification. Navigate package folder Install package following Installing package source steps. good idea create new branch work codebase. Read successful Git Branching Model branching convention.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"testing-the-package","dir":"Articles","previous_headings":"","what":"Testing the Package","title":"Testing the CRE Package","text":"Run following command test package.","code":"library(CRE)  # Generate sample data set.seed(1358) dataset <- generate_cre_dataset(n = 1000,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method = \"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\",                       offset = NULL)  hyper_params <- list(intervention_vars = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.01,                      t_corr = 1,                      t_pvalue = 0.05,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 10,                      subsample = 0.5)  # linreg CATE estimation with aipw ITE estimation cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            FASRC Daniela Maria Garcia. Author. Riccardo Cadei. Author. Kwonsang Lee. Author. Falco Joannes Bargagli Stoffi. Author.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Khoshnevis N, Garcia D, Cadei R, Lee K, Bargagli Stoffi F (2023). CRE: Interpretable Discovery Inference Heterogeneous Treatment Effects. R package version 0.2.5.9000, https://github.com/NSAPH-Software/CRE.","code":"@Manual{,   title = {CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},   author = {Naeem Khoshnevis and Daniela Maria Garcia and Riccardo Cadei and Kwonsang Lee and Falco Joannes {Bargagli Stoffi}},   year = {2023},   note = {R package version 0.2.5.9000},   url = {https://github.com/NSAPH-Software/CRE}, }"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"Identifying subgroups study population treatment exposure notably larger smaller effect outcome compared population average crucial social health sciences. estimating conditional average treatment effect (CATE) given pre-specified set covariates common approach, allows estimating causal effects subgroups specified priori researchers. package implements recently developed Causal Rule Ensemble (CRE) algorithm, flexible stable method interpretable discovery estimation heterogeneous treatment effects terms decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"package-details","dir":"","previous_headings":"","what":"Package Details","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"cre() function executes entire CRE algorithm utilizing data set input parameters user provides. divides study data discovery inference subsamples, generating filtering decision rules discovery subsample explain heterogeneity treatment effects. cre() uses inference subsample estimate linear decomposition Conditional Average Treatment Effect (CATE) terms just discovered decision rules, normal equations model ITE estimates. steps, cre() agnostic concerning method used ITE estimation. estimate Individual Treatment Effects (ITE), users can choose various methodologies vary terms speed accuracy. Several ITE estimators internally implemented (.e. AIPW, Causal Forest, Bayesian Causal Forest, Causal BART, S-Learner, T-Learner, X-Learner), customized ITE estimation can also provided input. results collected S3 object containing: number decision rules extracted step, CATE decomposition estimates corresponding uncertainty quantification, list selected parameters, predicted ITEs.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"applications","dir":"","previous_headings":"","what":"Applications","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"CRE package can used diverse theoretical practical applications long working binary exposure treatment level. example, can used education determine teaching methods effective specific student subgroups. marketing, can identify effective strategies different consumer subgroups. healthcare, can used identify patient subgroups benefit particular treatment intervention. can also used studies personalized medicine subgroup patients responds differently provided medicine. public health, CRE method applied discover subgroups vulnerable (resilient) causal effects long-term exposure air pollution mortality.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The CRE package — CRE-package","title":"The CRE package — CRE-package","text":"health social sciences, critically important identify subgroups study population treatment notable heterogeneity causal effects respect average treatment effect. Data-driven discovery heterogeneous treatment effects (HTE) via decision tree methods proposed task. Despite high interpretability, single-tree discovery HTE tends highly unstable find oversimplified representation treatment heterogeneity. accommodate shortcomings, propose Causal Rule Ensemble (CRE), new method discover heterogeneous subgroups ensemble--trees approach. CRE following features: provides interpretable representation HTE; 2) allows extensive exploration complex heterogeneity patterns; 3) guarantees high stability discovery. discovered subgroups defined terms interpretable decision rules, develop general two-stage approach subgroup-specific conditional causal effects estimation, providing theoretical guarantees.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The CRE package — CRE-package","text":"Bargagli-Stoffi, F. J., Cadei, R., Lee, K. Dominici, F. (2023). Causal rule ensemble: Interpretable Discovery Inference Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The CRE package — CRE-package","text":"Naeem Khoshnevis Daniela Maria Garcia Riccardo Cadei Kwonsang Lee Falco Joannes Bargagli Stoffi","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Sensitivity Analysis — analyze_sensitivity","title":"Sensitivity Analysis — analyze_sensitivity","text":"Method analyzing sensitivity estimates causal rule-specific treatment effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sensitivity Analysis — analyze_sensitivity","text":"","code":"analyze_sensitivity(ite_std, rules_matrix_std)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sensitivity Analysis — analyze_sensitivity","text":"ite_std standardized ITE rules_matrix_std standardized causal rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sensitivity Analysis — analyze_sensitivity","text":"list containing results sensitivity analysis","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cre object — autoplot.cre","title":"A helper function for cre object — autoplot.cre","text":"helper function plot cre object using ggplot2 package.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cre object — autoplot.cre","text":"","code":"# S3 method for cre autoplot(object, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cre object — autoplot.cre","text":"object cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cre object — autoplot.cre","text":"Returns ggplot object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_hyper_params","title":"Check input parameters — check_hyper_params","text":"Checks consistency input (hyper) parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_hyper_params","text":"","code":"check_hyper_params(X_names, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_hyper_params","text":"X_names observed covariates names. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_hyper_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_input","title":"Check input parameters — check_input","text":"Checks input parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_input","text":"","code":"check_input(y, z, X, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_input","text":"y observed response vector. z treatment vector. X covariate matrix. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_input","text":"list params might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input data — check_input_data","title":"Check input data — check_input_data","text":"Conducts sanity checks input data.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input data — check_input_data","text":"","code":"check_input_data(y, z, X, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input data — check_input_data","text":"y observed response vector. z treatment vector. X features matrix. ite estimated ITE vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input data — check_input_data","text":"number data samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check method-related parameters — check_method_params","title":"Check method-related parameters — check_method_params","text":"Checks method-related parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check method-related parameters — check_method_params","text":"","code":"check_method_params(y, ite, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check method-related parameters — check_method_params","text":"y observed response vector. ite estimated ITE vector. params list parameters required run method functions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check method-related parameters — check_method_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Causal rule ensemble — cre","title":"Causal rule ensemble — cre","text":"Performs Causal Rule Ensemble data set response variable, treatment variable, various features.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Causal rule ensemble — cre","text":"","code":"cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Causal rule ensemble — cre","text":"y observed response vector. z treatment vector. X covariate matrix (data frame). provided numerical values. method_params list parameters define models used, including: Parameters Honest Splitting ratio_dis: ratio data delegated rules discovery (default: 0.5). Parameters Discovery Inference ite_method: method ITE (pseudo-outcome) estimation (default: \"aipw\", options: \"aipw\" Augmented Inverse Probability Weighting, \"cf\" Causal Forest, \"bart\" Causal Bayesian Additive Regression Trees, \"slearner\" S-Learner, \"tlearner\" T-Learner, \"xlearner\" X-Learner, \"tpoisson\" T-Learner Poisson regression). learner_ps: model propensity score estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"bart\", \"cf\" ITE estimators). learner_y: model outcome estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"slearner\", \"tlearner\" \"xlearner\" ITE estimators). hyper_params list hyper parameters fine-tune method, including: General hyper parameters intervention_vars: Array intervention-able covariates names used Rules Generation. Empty null array means covariates considered intervention-able (default: NULL). ntrees: number decision trees random forest (default: 20). node_size: Minimum size trees' terminal nodes (default: 20). max_rules: Maximum number generated candidates rules (default: 50). max_depth: Maximum rules length (default: 3). t_decay: decay threshold rules pruning. Higher values carry aggressive pruning (default: 0.025). t_ext: threshold truncate generic specific (extreme) rules (default: 0.01, range: [0, 0.5)). t_corr: threshold define correlated rules (default: 1, range: [0,+inf)). stability_selection: Method stability selection selecting rules. \"vanilla\" stability selection, \"error_control\" stability selection error control  \"\" stability selection (default: \"vanilla\"). B: Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). subsample: Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). Method specific hyper parameters offset: Name covariate use offset (.e., \"x1\") T-Poisson ITE estimation. Use NULL offset used (default: NULL). cutoff:  Threshold (percentage) defining minimum cutoff value stability scores Stability Selection (default: 0.9). pfer: Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). ite estimated ITE vector. given ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Causal rule ensemble — cre","text":"S3 object composed : M number Decision Rules extracted step, CATE data.frame Conditional Average Treatment Effect decomposition estimates corresponding uncertainty quantification, method_params list method parameters, hyper_params list hyper parameters, rules list rules (implicit form) decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Causal rule ensemble — cre","text":"intervention_vars provided, important note individual treatment effect still computed using covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Causal rule ensemble — cre","text":"","code":"# \\donttest{ set.seed(123) dataset <- generate_cre_dataset(n = 400,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = NULL,                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 20,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) #> Loading required package: nnls #> Registered S3 method overwritten by 'randomForest': #>   method      from #>   plot.margin RRF  # }"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":null,"dir":"Reference","previous_headings":"","what":"The Causal Rule Ensemble Crossfit — cre_crossfit","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"Performs Causal Rule Ensemble twice dataset response variable, treatment variable, various features, swapping discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"cre_crossfit(   y,   z,   X,   ite_method_dis,   include_ps_dis = NA,   ps_method_dis = \"SL.xgboost\",   or_method_dis = NA,   ite_method_inf,   include_ps_inf = NA,   ps_method_inf = \"SL.xgboost\",   or_method_inf = NA,   ntrees_rf,   ntrees_gbm,   min_nodes,   max_nodes,   t,   q,   rules_method = NA,   include_offset = FALSE,   offset_name = NA,   cate_method = \"DRLearner\",   cate_SL_library = \"SL.xgboost\",   filter_cate = FALSE )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"y observed response vector z treatment vector X covariate matrix ite_method_dis method estimate discovery sample ITE include_ps_dis whether include propensity score estimate covariate discovery ITE estimation, considered BART, XBART, CF ps_method_dis estimation model propensity score discovery subsample or_method_dis estimation model outcome regressions estimate_ite_aipw discovery subsample ite_method_inf method estimate inference sample ITE include_ps_inf whether include propensity score estimate covariate inference ITE estimation, considered BART, XBART, CF ps_method_inf estimation model propensity score inference subsample or_method_inf estimation model outcome regressions estimate_ite_aipw inference subsample ntrees_rf number decision trees randomForest ntrees_gbm number decision trees gradient boosting min_nodes minimum size trees' terminal nodes max_nodes maximum size trees' terminal nodes t common support used generating causal rules matrix q selection threshold used selecting causal rules rules_method method selecting causal rules binary outcomes include_offset whether include offset estimating ITE, poisson offset_name name offset, included cate_method method estimate CATE values cate_SL_library library used cate_method set DRLearner filter_cate whether filter rules p-value <= 0.05","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"S3 object containing matrix averaged Conditional Average Treatment Effect estimates two CRE iterations","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  cre_cross_results <- cre_crossfit(y = dataset[[\"y\"]], z = dataset[[\"z\"]],                                   X = as.data.frame(dataset[[\"X\"]]),                                   ite_method_dis = \"bart\",                                   include_ps_dis = TRUE,                                   ite_method_inf = \"bart\",                                   include_ps_inf = TRUE,                                   ntrees_rf = 100, ntrees_gbm = 50,                                   min_nodes = 20, max_nodes = 5,                                   t = 0.025, q = 0.8) #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> Conducting Discovery Subsample Analysis 1 #> 411 rules (length<=15) were extracted from the first 100 trees. #> 150 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 1 #> Conducting Discovery Subsample Analysis 2 #> 493 rules (length<=15) were extracted from the first 100 trees. #> 100 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 2"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"Discard rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"","code":"discard_anomalous_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"Discard redundant correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"","code":"discard_correlated_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover causal decision rules — discover_causal_rules","title":"Discover causal decision rules — discover_causal_rules","text":"Discovers Causal Decision Rules randomization-based tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover causal decision rules — discover_causal_rules","text":"","code":"discover_causal_rules(   rules_matrix,   rules_list,   ite,   stability_selection,   cutoff,   pfer,   penalty_rl )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover causal decision rules — discover_causal_rules","text":"rules_matrix causal rules matrix. rules_list vector causal rules. ite estimated ITE. stability_selection Whether using stability selection select causal rules. cutoff Threshold (percentage) defining minimum cutoff value stability scores. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). penalty_rl Order penalty rules length LASSO Causal Rules Discovery (.e. 0: penalty, 1: rules_length, 2: rules_length^2)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover causal decision rules — discover_causal_rules","text":"List Causal Decision Rules discovered","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover rules — discover_rules","title":"Discover rules — discover_rules","text":"Discover minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover rules — discover_rules","text":"","code":"discover_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover rules — discover_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover rules — discover_rules","text":"minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Conditional Average Treatment Effect — estimate_cate","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"Estimates Conditional Average Treatment Effect (CATE) linearly modeling Individual Treatment Effect set rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"","code":"estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"rules_matrix rules matrix. rules_explicit list select rules terms covariate names. ite estimated ITEs. B number bootstrap samples uncertainty quantification estimation. subsample bootstrap ratio subsample uncertainty quantification estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"list 2 elements: summary: data frame summarizing CATE linear decomposition: Rule: rule name, Estimate: linear contribution CATE, CI_lower: lower bound 95% confidence interval estimate, CI_upper: upper bound 95% confidence interval estimate, P_Value: p-value (Z-test). model: linear model CATE-ATE estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"Estimates Individual Treatment Effect given response vector, treatment vector, covariate matrix, desired algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"","code":"estimate_ite(y, z, X, ite_method, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"y observed response vector. z observed treatment vector. X covariate matrix. ite_method method estimating Individual Treatment Effect. methods requires additional parameters. parameters mentioned indented blocks method definitions provided end parameters list. \"slearner\": S-Learner. - learner_y \"tlearner\": T-Learner. learner_y \"tpoisson\": T-Poisson. offset \"xlearner\": X-Learner. learner_y \"aipw\": Augmented Inverse Probability Weighting. learner_ps  learner_y \"bart\": Bayesian Additive Regression Trees. learner_ps \"cf\": Causal Forest. learner_ps ... Additional parameters passed different models.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"list ITE estimates.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"additional-parameters","dir":"Reference","previous_headings":"","what":"Additional parameters","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"learner_ps: estimation method propensity score. includes libraries SuperLearner package. learner_y: estimation model outcome. includes libraries SuperLearner package. offset: Name covariate use offset (.e. \"x1\") Poisson ITE Estimation. NULL offset used.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"Estimates Individual Treatment Effect using Augmented Inverse Probability Weighting given response vector, treatment vector, features matrix, estimation model propensity score estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"","code":"estimate_ite_aipw(y, z, X, learner_ps = \"SL.xgboost\", learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"y observed response vector. z treatment vector. X features matrix. learner_ps estimation model propensity score. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"Estimates Individual Treatment Effect using Bayesian Additive Regression Trees given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"","code":"estimate_ite_bart(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"y observed response vector. z treatment vector. X features matrix. learner_ps Method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"number samples number burn set default equal 500.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"Estimates Individual Treatment Effect using Bayesian Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"","code":"estimate_ite_bcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"y observed response vector. z treatment vector. X features matrix. ps_method method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"Estimates Individual Treatment Effect using Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"","code":"estimate_ite_cf(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"y observed response vector. z treatment vector. X features matrix. learner_ps method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"Estimates Individual Treatment Effect (ITE) using Inverse Propensity Weighting (IPW) given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"","code":"estimate_ite_ipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"Method estimating Individual Treatment Effect using Outcome Regression given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"estimate_ite_or(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"y observed response vector z treatment vector X features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  # Initialize parameters y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- as.data.frame(dataset[[\"X\"]])  ite_list <- estimate_ite_or(y, z, X) #> *****Into main of wbart #> *****Data: #> data:n,p,np: 388, 10, 612 #> y1,yn: -0.694294, 1.362455 #> x1,x[n*p]: 1.000000, 1.000000 #> xp1,xp[np*p]: 0.000000, 0.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.118817,3.000000,0.250422 #> *****sigma: 1.133838 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000 #> *****Into main of wbart #> *****Data: #> data:n,p,np: 612, 10, 388 #> y1,yn: -0.485955, 0.857738 #> x1,x[n*p]: 0.000000, 0.000000 #> xp1,xp[np*p]: 1.000000, 1.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.139249,3.000000,0.168737 #> *****sigma: 0.930723 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"Estimates Individual Treatment Effect (ITE) using Outcome Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"","code":"estimate_ite_oreg(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"y observed response vector. z treatment vector. X features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"Estimates Individual Treatment Effect using Poisson Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"","code":"estimate_ite_poisson(y, z, X, X_names, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"y observed response vector. z treatment vector. X features matrix. X_names vector includes names covariates. offset name covariate use offset (.e. 'x1') model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"Estimates Individual Treatment Effect using Stabilized Inverse Propensity Weighting given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"","code":"estimate_ite_sipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"Estimates Individual Treatment Effect using S-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"","code":"estimate_ite_slearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"Estimates Individual Treatment Effect using T-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"","code":"estimate_ite_tlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"Estimates Individual Treatment Effect using Poisson regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"","code":"estimate_ite_tpoisson(y, z, X, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"y observed response vector. z treatment vector. X features matrix. offset name covariate use offset (.e. “x1”) model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"","code":"estimate_ite_xbart(y, z, X, include_ps, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"y observed response vector z treatment vector X features matrix include_ps whether include propensity score estimate covariate ITE estimation ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Causal Forest given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"","code":"estimate_ite_xbcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"y observed response vector z treatment vector X features matrix ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"Estimates Individual Treatment Effect using X-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"","code":"estimate_ite_xlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the propensity score — estimate_ps","title":"Estimate the propensity score — estimate_ps","text":"Estimates Propensity Score given treatment vector features data frame.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the propensity score — estimate_ps","text":"","code":"estimate_ps(z, X, ps_method = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the propensity score — estimate_ps","text":"z treatment vector. X features data frame. ps_method estimation model propensity score (default: SL.xgboost).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the propensity score — estimate_ps","text":"vector propensity score estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":null,"dir":"Reference","previous_headings":"","what":"Discovery (performance) evaluation — evaluate","title":"Discovery (performance) evaluation — evaluate","text":"Computes different metrics discovery evaluation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discovery (performance) evaluation — evaluate","text":"","code":"evaluate(ground_truth, prediction)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discovery (performance) evaluation — evaluate","text":"ground_truth List true (CDR/EM) values. prediction List predicted (CDR/EM) values.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discovery (performance) evaluation — evaluate","text":"Intersection union, precision, recall.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract effect modifiers — extract_effect_modifiers","title":"Extract effect modifiers — extract_effect_modifiers","text":"Extracts effect modifiers list (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract effect modifiers — extract_effect_modifiers","text":"","code":"extract_effect_modifiers(rules_list, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract effect modifiers — extract_effect_modifiers","text":"rules_list list (causal) decision rules. X_names list covariate names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract effect modifiers — extract_effect_modifiers","text":"list effect modifiers.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract (causal) decision rules — extract_rules","title":"Extract (causal) decision rules — extract_rules","text":"Extracts causal rules random forest gradient boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract (causal) decision rules — extract_rules","text":"","code":"extract_rules(treelist, X, max_depth, digits = 2)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract (causal) decision rules — extract_rules","text":"treelist list decision trees. X Features matrix. max_depth number top levels tree considered. digits Number digits rounding decision rules extract conditions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract (causal) decision rules — extract_rules","text":"vector (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter correlated rules — filter_correlated_rules","title":"Filter correlated rules — filter_correlated_rules","text":"Discards highly correlated rules (.e., \\(Cov(rule_1,rule_2) > t_{corr}\\)).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter correlated rules — filter_correlated_rules","text":"","code":"filter_correlated_rules(rules_matrix, rules_list, t_corr)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter correlated rules — filter_correlated_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_corr threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter correlated rules — filter_correlated_rules","text":"rules matrix without highly correlated rules (columns).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter extreme decision rules — filter_extreme_rules","title":"Filter extreme decision rules — filter_extreme_rules","text":"Discards rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter extreme decision rules — filter_extreme_rules","text":"","code":"filter_extreme_rules(rules_matrix, rules_list, t_ext)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_ext threshold [0, 0.5) range truncate generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules matrix without rare/common rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"Filters irrelevant decision rules. irrelevant rules interpreted error increase removing variable-value pair decision rules (see “Interpreting tree ensembles inTrees package” Houtao Deng, 2019).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"","code":"filter_irrelevant_rules(rules, X, ite, t_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"rules list rules. X features matrix. ite estimated ITE. t_decay decay threshold rules pruning.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"list “relevant” rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate causal rules — generate_causal_rules","title":"Generate causal rules — generate_causal_rules","text":"Generates causal decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate causal rules — generate_causal_rules","text":"","code":"generate_causal_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate causal rules — generate_causal_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate causal rules — generate_causal_rules","text":"vector causal rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate CRE synthetic data — generate_cre_dataset","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Generates synthetic data sets run simulation causal inference experiments composed outcome vector (y), treatment vector (z), covariates matrix (X), unobserved individual treatment effects vector (ite). arguments specify data set characteristic, including number individuals (n), number covariates (p), correlation within covariates (rho),  number decision rules (n_rules) decomposing Conditional Average Treatment Effect (CATE), treatment effect magnitude (effect_size), confounding mechanism (confounding), whether covariates outcomes binary continuous (binary_covariates, binary_outcome).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"generate_cre_dataset(   n = 1000,   rho = 0,   n_rules = 2,   p = 10,   effect_size = 2,   binary_covariates = TRUE,   binary_outcome = TRUE,   confounding = \"no\" )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate CRE synthetic data — generate_cre_dataset","text":"n integer number represents number observations. Non-integer values converted integer number. rho positive double number represents correlation within covariates (default: 0, range: [0,1)). n_rules number causal rules (default: 2, range: {1,2,3,4}). p number covariates (default: 10). effect_size treatment effect size magnitude (default: 2, range: \\(\\geq\\)0). binary_covariates Whether use binary continuous covariates (default: TRUE). binary_outcome Whether use binary continuous outcomes (default: TRUE). confounding continuous outcome, add confounding variables: \"lin\" linear confounding, \"nonlin\" non-linear confounding, \"\" confounding (default).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate CRE synthetic data — generate_cre_dataset","text":"list, representing generated synthetic data set, containing: y outcome vector, z treatment vector, X covariates matrix, ite individual treatment vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate CRE synthetic data — generate_cre_dataset","text":"covariates matrix generated specified correlation among individuals, covariate sampled either Bernoulli(0.5) binary, Gaussian(0,1) continuous. treatment vector sampled Bernoulli(\\(\\frac{1}{1+ \\exp(1-x_1+x_2-x_3)}\\)), enforcing treatment assignment probabilities function observed covariates. potential outcomes (\\(y(0)\\) \\(y(1)\\)) sampled Bernoulli binary, Gaussian (standard deviation equal 1) continuous. mean equal confounding term (null, linear non-linear always null binary outcome) plus 1-4 decision rules weighted treatment effect magnitude. two potential outcomes characterizes CATE (unobserved individual treatment effects vector) sum different additive contributions decision rules considered (plus intercept). final expression CATE depends treatment effect magnitude number decision rules considered. 4 decision rules : Rule 1: \\(1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x})\\) Rule 2: \\(1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})\\) Rule 3: \\(1\\{x_4 \\leq 0.5\\}(\\textbf{x})\\) Rule 4: \\(1\\{x_5 \\leq 0.5; x_7 > 0.5; x_8 \\leq 0.5\\}(\\textbf{x})\\) corresponding additive average treatment effect (AATE) equal : Rule 1: \\(-\\) effect_size, Rule 2: \\(+\\) effect_size, Rule 3: \\(- 0.5 \\cdot\\) effect_size, Rule 4: \\(+ 2 \\cdot\\) effect_size. example, setting effect_size=4 n_rules=2: $$\\text{CATE}(\\textbf{x}) = -4 \\cdot 1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x}) + 4 \\cdot 1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})$$ final outcome vector y finally computed combining potential outcomes according treatment assignment.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Set covariates domain (binary_covariates) outcome domain (binary_outcome) according experiment interest. Increase complexity heterogeneity discovery: decreasing sample size (n), adding correlation among covariates (rho), increasing number rules (n_rules), increasing number covariates (p), decreasing absolute value causal effect (effect_size), adding linear -linear confounders (confounding).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"set.seed(123) dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary_covariates = TRUE,                                 binary_outcome = TRUE, confounding = \"no\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules — generate_rules","title":"Generate rules — generate_rules","text":"Generates list rules characterizing heterogeneity Conditional Average Treatment Effect (CATE) tree-based methods (.e., random forest).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules — generate_rules","text":"","code":"generate_rules(X, ite, ntrees, node_size, max_rules, max_depth)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules — generate_rules","text":"X covariate matrix. ite vector estimated ITE. ntrees number decision trees random forest algorithm. node_size Minimum size trees' terminal nodes. max_rules Maximum number candidate decision rules. max_depth Maximum rules length.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules — generate_rules","text":"list rules (names).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules matrix — generate_rules_matrix","title":"Generate rules matrix — generate_rules_matrix","text":"Generates rules matrix feature covariate matrix vector rules. number rows rules_matrix equal number samples X, number columns equal number rules rules_list. element rules_matrix corresponds specific data sample rule. data sample satisfies rule, corresponding element rules_matrix set 1. Otherwise, element set 0.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules matrix — generate_rules_matrix","text":"","code":"generate_rules_matrix(X, rules_list)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules matrix — generate_rules_matrix","text":"X Features matrix. rules_list vector rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules matrix — generate_rules_matrix","text":"causal rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Logger settings — get_logger","title":"Get Logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":null,"dir":"Reference","previous_headings":"","what":"Honest splitting — honest_splitting","title":"Honest splitting — honest_splitting","text":"Splits data discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Honest splitting — honest_splitting","text":"","code":"honest_splitting(y, z, X, ratio_dis, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Honest splitting — honest_splitting","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis double number indicating ratio data delegated discovery sub-sample. ite vector estimated ITE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Honest splitting — honest_splitting","text":"list containing discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret rules — interpret_rules","title":"Interpret rules — interpret_rules","text":"Replaces column numbers rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret rules — interpret_rules","text":"","code":"interpret_rules(rules, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret rules — interpret_rules","text":"rules vector rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret rules — interpret_rules","text":"list explicit (human-readable) rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret select rules — interpret_select_rules","title":"Interpret select rules — interpret_select_rules","text":"Replaces column numbers select rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret select rules — interpret_select_rules","text":"","code":"interpret_select_rules(select_rules_dis, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret select rules — interpret_select_rules","text":"select_rules_dis vector select causal rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret select rules — interpret_select_rules","text":"vector select causal rules interpretable.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"","code":"lasso_rules_filter(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q Number (unique) selected rules per subsample stability selection. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cre class — plot.cre","title":"Extend generic plot functions for cre class — plot.cre","text":"wrapper function extend generic plot functions cre class.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cre class — plot.cre","text":"","code":"# S3 method for cre plot(x, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cre class — plot.cre","text":"x cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cre class — plot.cre","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict individual treatment effect via causal rule ensemble — predict.cre","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"Predicts individual treatment effect via causal rule ensemble algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"","code":"# S3 method for cre predict(object, X, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"object cre object running CRE function. X covariate matrix (data.frame) ... Additional arguments passed customize prediction.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"array estimated Individual Treatment Effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for the CRE object — print.cre","title":"Extend print function for the CRE object — print.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for the CRE object — print.cre","text":"","code":"# S3 method for cre print(x, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for the CRE object — print.cre","text":"x cre object running CRE function. verbose Set level results description details: 0 results summary, 1 results parameters summary, 2 results parameters rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for the CRE object — print.cre","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune (Causal) Decision Rules — prune_rules","title":"Prune (Causal) Decision Rules — prune_rules","text":"Method pruning (Causal) Decision Rules extracted Random Forest Gradient Boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune (Causal) Decision Rules — prune_rules","text":"","code":"prune_rules(rules, X, ite_std, max_decay, type_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune (Causal) Decision Rules — prune_rules","text":"rules list decision rules. X features matrix. ite_std standardized ITE. max_decay Decay Threshold pruning rules. type_decay Decay Type pruning rules (1: relative error; 2: error).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune (Causal) Decision Rules — prune_rules","text":"list (Causal) Decision Rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select Causal Rules — select_causal_rules","title":"Select Causal Rules — select_causal_rules","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select Causal Rules — select_causal_rules","text":"","code":"select_causal_rules(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select Causal Rules — select_causal_rules","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q selection threshold used selecting causal rules. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select Causal Rules — select_causal_rules","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select rules — select_rules","title":"Select rules — select_rules","text":"Given set rules, selects minimal set linearly decomposing Conditional Average Treatment Effect (CATE) LASSO (optionally Stability Selection).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select rules — select_rules","text":"","code":"select_rules(rules_matrix, rules, ite, stability_selection, cutoff, pfer, B)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select rules — select_rules","text":"rules_matrix rules matrix. rules list rules. ite estimated ITE. stability_selection Stability selection method. cutoff Threshold (percentage) defining minimum cutoff value stability scores. stability selection. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). stability selection. B Number bootstrap samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select rules — select_rules","text":"minimal set rules linearly decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Logger settings — set_logger","title":"Set Logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"CRE.log\", logger_level = \"INFO\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: CRE.log) logger_level log level. log level set, log levels also activated (implemented). Available levels include: TRACE: Provides verbose detailed logging, including steps taken achieve result, often used debugging. Activating TRACE also enable DEBUG, INFO, SUCCESS, WARN, ERROR, FATAL logs. DEBUG: Provides detailed logging flow application, used mostly developers understand potential issues. Activating DEBUG also enable INFO, SUCCESS, WARN, ERROR, FATAL logs. INFO (Default): Standard messages inform user normal operation system. Activating INFO also enable SUCCESS, WARN, ERROR, FATAL logs. SUCCESS: Messages indicating successful completion particular operation task. Activating SUCCESS also enable WARN, ERROR, FATAL logs. WARN: Warning messages events might cause problems future, yet errors. Activating WARN also enable ERROR FATAL logs. ERROR: Reports error due system may able achieve functionality, application halt. Activating ERROR also enable FATAL logs. FATAL: Reports severe error events presumably lead application abort.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set Logger settings — set_logger","text":"Log levels specified developers initial implementation. Future developers contributors can leverage log levels better capture document application's processes events.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Logger settings — set_logger","text":"","code":"set_logger(\"Debug\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Data — split_data","title":"Split Data — split_data","text":"Splits data discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Data — split_data","text":"","code":"split_data(y, z, X, ratio_dis)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Data — split_data","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis ratio data delegated discovery subsample","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Data — split_data","text":"list containing discovery inference subsamples","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardize Rules Matrix — standardize_rules_matrix","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardize (.e. mean=0 stdev=1) rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"","code":"standardize_rules_matrix(rules_matrix)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"rules_matrix rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardized rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of CRE object — summary.cre","title":"Print summary of CRE object — summary.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of CRE object — summary.cre","text":"","code":"# S3 method for cre summary(object, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of CRE object — summary.cre","text":"object cre object running CRE function. verbose Set level results description details: results summary 0, results+parameters summary 1, results+parameters+rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of CRE object — summary.cre","text":"summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":null,"dir":"Reference","previous_headings":"","what":"Take 1 — take1","title":"Take 1 — take1","text":"Generate list indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take 1 — take1","text":"","code":"take1(len)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take 1 — take1","text":"len length value","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take 1 — take1","text":"vector indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take 1 — take1","text":"","code":"ind <- CRE:::take1(10)"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-5","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.5 (2023-12-6)","text":"Add (vanilla) Stability Selection (without Error Control). max_rules hyper parameters max rules filtering. Uncertainty Quantification estimation bootstrapping. B hyper-parameter, subsample hyper-parameter. rules(implicit form) cre() function return. predict() function ITE estimation via CRE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-5","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.5 (2023-12-6)","text":"Type stability_selection binary -> string (‘’,‘vanilla’,‘error_control’). Unify ntrees_gbm hyper-parameter ntrees_gbm hyper-parameter ntrees hyper-parameter. rules generation retrieve decision rules also internal nodes, just terminal nodes. ite_method_dis, ite_method_inf method-parameter -> ite_method. ps_method_dis, ps_method_inf method-parameter -> learner_ps. oreg_method_dis, oreg_method_inf method-parameter -> learner_y.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-5","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.5 (2023-12-6)","text":"max_nodes hyper-parameter. Remove rules generation Generalized Boosted Regression. replace hyper-parameter. penalty_rl hyper-parameter. t_pvalue hyper-parameter. ite_pred cre() function return.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.5 (2023-12-6)","text":"Error saving covariates name CRE result using intervention_vars.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-024-2023-6-14","dir":"Changelog","previous_headings":"","what":"CRE 0.2.4 (2023-6-14)","title":"CRE 0.2.4 (2023-6-14)","text":"CRAN release: 2023-06-14","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-4","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.4 (2023-6-14)","text":"Method paper description updated.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-023-2023-4-27","dir":"Changelog","previous_headings":"","what":"CRE 0.2.3 (2023-4-27)","title":"CRE 0.2.3 (2023-4-27)","text":"CRAN release: 2023-04-27","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-3","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.3 (2023-4-27)","text":"Bayesian Causal Forest (bcf) ITE estimator supported.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-022-2023-4-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.2 (2023-4-17)","title":"CRE 0.2.2 (2023-4-17)","text":"CRAN release: 2023-04-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-2","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.2 (2023-4-17)","text":"Fixed failing unit tests specific operating systems.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-021-2023-3-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.1 (2023-3-17)","title":"CRE 0.2.1 (2023-3-17)","text":"CRAN release: 2023-03-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.1 (2023-3-17)","text":"Replace BATE ATE CATE Linear Decomposition. Update plot() function (remove ATE, old BATE, explicit AATEs).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.1 (2023-3-17)","text":"Code Conduct.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.1 (2023-3-17)","text":"Causal Tree benchmark functional tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.1 (2023-3-17)","text":"Rank-Deficient Rule Matrix Issue (redundant rules). Intervention Variables Filtering (ordered filtering).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-020-2023-1-19","dir":"Changelog","previous_headings":"","what":"CRE 0.2.0 (2023-1-19)","title":"CRE 0.2.0 (2023-1-19)","text":"CRAN release: 2023-01-19","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.0 (2023-1-19)","text":"offset method-parameter -> hyper-parameter estimate_ite_poisson function -> estimate_ite_tpoisson max_dacay hyper-parameter -> t_decay. interpret_select_rules function -> interpret_rules. generate_causal_rules function -> discover_rules. discover_causal_rules function ->select_rules. offset_name method parameter -> offset. Hyper method parameters required arguments cre. cre object: added parameters ite estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.0 (2023-1-19)","text":"Synthetic data set 1 3 rules (generate_cre_dataset). S-Learner (slearner) method ITE estimation. T-Learner (tlearner) method ITE estimation. X-Learner (xlearner) method ITE estimation. Rules Selection description summary.cre. verbose parameter summary.cre. ite, additional cre input parameter use personalized ite estimations. Default values hyper parameters. Default values method parameters. Simulation experiments estimation (estimation.R). Simulation experiments discovery (discovery.R). extract_effect_modifiers function (utility performance evaluation). evaluate function discovery evaluation. confounding parameter generate_cre_dataset set confounding type. ite_pred model CRE results. binary_covariates parameter generate_cre_dataset set covariates domain.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.0 (2023-1-19)","text":"include_ps_inf method-parameter. include_ps_dis method-parameter. oreg method ITE estimation. ipw method ITE estimation. sipw method ITE estimation. ITE standard deviation estimation. type_decay hyper-parameter. Keep linreg CATE estimation (remove cate_method cate_SL_library parameters). method_params hyper_params additional parameters summary.cre. ite standardization Rules Generation. random_state parameter. include_offset method parameter.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.0 (2023-1-19)","text":"Rules Generation Issue (set rules length fix bootstrapping).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-011-2022-10-18","dir":"Changelog","previous_headings":"","what":"CRE 0.1.1 (2022-10-18)","title":"CRE 0.1.1 (2022-10-18)","text":"CRAN release: 2022-10-22","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.1 (2022-10-18)","text":"binary parameter generate_cre_dataset -> binary_outcome . filter_cate hyper-parameter -> t_pvalue. t_anom hyper-parameter -> t_ext. effect_modifier hyper-parameter -> intervention_vars. lasso_rules_filter function -> discover_causal_rules. split_data function -> honest_splitting. prune_rules function -> `filter_irrelevant_rules. discard_correlated_rules function -> filter_correlated_rules. discard_anomalous_rules function -> filter_extreme_rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.1 (2022-10-18)","text":"Weighted LASSO Causal Rules Discovery (penalty_rl hyper-parameter).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-010-2022-10-17","dir":"Changelog","previous_headings":"","what":"CRE 0.1.0 (2022-10-17)","title":"CRE 0.1.0 (2022-10-17)","text":"CRAN release: 2022-10-18","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.0 (2022-10-17)","text":"Update examples tests functions. q hyper-parameter -> cutoff. pfer_val hyper-parameter -> pfer. select_causal_rules function -> lasso_rules_filter. t hyper-parameter -> t_anom. Separate standardization, remove filtering generate_rules_matrix function. summary.cre function describe results. min_nodes hyper-parameter -> node_size (randomForest convention). cre returns S3 object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.0 (2022-10-17)","text":"Examples tests functions. prune_rules function discard un-predictive rules. discard_anomalous_rules function discard anomalous rules (see t_corr hyper-parameter.). discard_correlated_rules function discard correlated rules (see t_anom hyper-parameter). effect_modifiers parameter generate_rules function covariates filtering. generate_causal_rules function. Helper function SuperLearner package propensity score estimation estimate_ite_xyz. Five methods CATE estimation (poisson, DRLearner, bart-baggr, cf-means, linreg) estimate_cate function. (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) method-parameters complement SuperLearner package. cate_method method-parameter select CATE estimation method. filter_cate method-parameter estimation filtering. p parameter (generate_cre_dataset function) set number covariates. replace parameter (generate_rules function) allow bootstrapping. cre.print generic function print cre S3 object results. cre.summary generic functions summarize cre S3 object Results. check_input function isolate input checks. estimate_ite_aipw function augmented inverse propensity weighting. plot.cre generic function plot cre S3 object results. test-cre_functional.R test functionality package. stability_selection function causal rules selection.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-1-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.1.0 (2022-10-17)","text":"estimate_ite_blp function. take1() function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.1.0 (2022-10-17)","text":"Undesired ‘’ Decision Rule Issue. Causal Rule Selected Issue.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.0.1 (2021-10-20)","text":"estimate_cate include two methods estimating CATE values. cre added initial checks binary outcome whether include propensity score ITE estimation. estimate_ite_xyz conduct propensity score estimation using helper function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.0.1 (2021-10-20)","text":"Example generate_cre_dataset. set_logger get_logger. check_input_data function. generate_cre_dataset function generate synthetic data testing package. test-generate_cre_dataset function test. estimate_ps function estimate propensity score. estimate_ite_xbart function generate ITE estimates using accelerated BART. estimate_ite_xbcf function generate ITE estimates using accelerated BCF. analyze_sensitivity function conduct sensitivity analysis unmeasured confounding. cre function perform entire Causal Rule Ensemble method. estimate_cate function generate CATE estimates ITE estimates select rules. estimate_ite function generate ITE estimates using user-specified method (calls estimate_ite_xyz functions). estimate_ite_bart function generate ITE estimates using BART. estimate_ite_bcf function generate ITE estimates using Bayesian Causal Forests. estimate_ite_cf function generate ITE estimates using Causal Forests. estimate_ite_ipw function generate ITE estimates using IPW. estimate_ite_or function generate ITE estimates using Outcome Regression. estimate_ite_sipw function generate ITE estimates using SIPW. extract_rules function extract list causal rules randomForest GBM models. generate_rules function generate causal rule models using randomForest GBM methods. generate_rules_matrix function convert list causal rules matrix. select_causal_rules function apply penalized regression causal rules. select important ones. split_data function split input data discovery inference subsamples. take1 function create subsample indices.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-0-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.0.1 (2021-10-20)","text":"seed argument generate_cre_datase function.","code":""}]
+[{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement nkhoshnevis@g.harvard.edu. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"CRE","text":"Installing CRAN. Installing latest developing version. Import.","code":"install.packages(\"CRE\") library(devtools) install_github(\"NSAPH-Software/CRE\", ref = \"develop\") library(\"CRE\")"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"arguments","dir":"Articles","previous_headings":"","what":"Arguments","title":"CRE","text":"Data (required) y observed response/outcome vector (binary continuous). z treatment/exposure/policy vector (binary). X covariate matrix (binary continuous). Parameters (required) method_parameters list parameters define models used, including: - ratio_dis ratio data delegated discovery sub-sample (default: 0.5). - ite_method method estimate individual treatment effect (default: “aipw”) [1]. - learner_ps (SuperLearner) model propensity score estimation (default: “SL.xgboost”, used “aipw”,“bart”,“cf” ITE estimators). - learner_y (SuperLearner) model outcome estimation (default: “SL.xgboost”, used “aipw”,“slearner”,“tlearner” “xlearner” ITE estimators). hyper_params list hyper parameters fine tune method, including: - intervention_vars Intervention-able variables used Rules Generation (default: NULL). - ntrees number decision trees random forest (default: 20). - node_size Minimum size trees’ terminal nodes (default: 20). - max_rules Maximum number candidate decision rules (default: 50). - max_depth Maximum rules length (default: 3). - t_decay decay threshold rules pruning (default: 0.025). - t_ext threshold define generic specific (extreme) rules (default: 0.01). - t_corr threshold define correlated rules (default: 1). - stability_selection Method stability selection selecting rules. vanilla stability selection, error_control stability selection error control stability selection (default: vanilla). - B Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). - subsample Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). - offset Name covariate use offset (.e. “x1”) T-Poisson ITE Estimation. NULL used (default: NULL). - cutoff Threshold defining minimum cutoff value stability scores Stability Selection (default: 0.9). - pfer Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). Additional Estimates (required) ite estimated ITE vector. given, ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"options-for-the-ite-estimation","dir":"Articles","previous_headings":"Arguments > Notes","what":"Options for the ITE estimation","title":"CRE","text":"[1] Options ITE estimation follows: - S-Learner (slearner). - T-Learner (tlearner) - T-Poisson(tpoisson) - X-Learner (xlearner) - Augmented Inverse Probability Weighting (aipw) - Causal Forests (cf) - Causal Bayesian Additive Regression Trees (bart) estimates ITE provided ite additional argument, ITE estimations discovery inference skipped values estimates used instead. ITE estimator requires also outcome learner /propensity score learner SuperLearner package (.e., “SL.lm”, “SL.svm”). models simple classifiers/regressors. default XGBoost algorithm used steps.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"customized-wrapper-for-superlearner","dir":"Articles","previous_headings":"Arguments > Notes","what":"Customized wrapper for SuperLearner","title":"CRE","text":"One can create customized wrapper SuperLearner internal packages. following example providing number cores (e.g., 12) xgboost package shared memory system. use “m_xgboost”, instead “SL.xgboost”.","code":"m_xgboost <- function(nthread = 12, ...) {   SuperLearner::SL.xgboost(nthread = nthread, ...) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"CRE","text":"Example 1 (default parameters) Example 2 (personalized ite estimation) Example 3 (setting parameters) synthetic data sets can generated using generate_cre_dataset().","code":"set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  cre_results <- cre(y, z, X) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\")   y <- dataset[[\"y\"]]   z <- dataset[[\"z\"]]   X <- dataset[[\"X\"]]  ite_pred <- ... # personalized ite estimation cre_results <- cre(y, z, X, ite = ite_pred) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = c(\"x1\",\"x2\",\"x3\",\"x4\"),                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.8,                      pfer = 1,                      B = 10,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"environment-setup","dir":"Articles","previous_headings":"","what":"Environment Setup","title":"Contribution","text":"Please follow steps get copy CRE Github account. Navigate CRE Github repository, top right corner, click Fork button. add clone project Github account. Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): already SSH key, need generate one. Read . Now, can modify codebase track modification. good idea create new branch work codebase. Read following instructions git branching.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"git-branching-model","dir":"Articles","previous_headings":"","what":"Git Branching Model","title":"Contribution","text":"Although, personal repository, can pick branch name, however, order keep consistency also understand working , following convention strongly recommended. project, follow convention proposed Vincent Driessen successful Git branching model post.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"where-to-submit-pull-requests","dir":"Articles","previous_headings":"","what":"Where to submit pull requests?","title":"Contribution","text":"pull requests submitted base repository: fasrc/CRE base: develop branch.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"pull-request-checklist","dir":"Articles","previous_headings":"","what":"Pull request checklist","title":"Contribution","text":"Please run devtools::document(), devtools::load_all() final modifications. Make sure modified code passes checks tests (can run devtools::check() RStudio) PR pass CI reviews can merge . Add line(s) modification NEWS.md file. adding new features, please make sure appropriate documentation added updated. Please clean white spaces. Read .","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"reporting-bugs","dir":"Articles","previous_headings":"","what":"Reporting bugs","title":"Contribution","text":"Please report potential bugs creating new issue sending us email. Please include following information bug report: brief description , expected happen, happened. OS using whether using personal computer HPC cluster. version package installed.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"style-guide","dir":"Articles","previous_headings":"","what":"Style Guide","title":"Contribution","text":"project, follow tidyverse style guide.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"names","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Names","title":"Contribution","text":"File names snake_case ends .R (e.g., create_matching.R) variable names small letter separate _ need (e.g., delta_n) Function names follow snake_case style (e.g., generate_data) Function names follow verb+output convention (e.g., compute_resid)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"spaces-and-indentation","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Spaces and Indentation","title":"Contribution","text":"Indentations two spaces (use tab) Place space around binary operators (e.g., x + y) Place space comma Place space # commenting avoid multiple ### put space opening closing parenthesis Place space () used , , .","code":"#Acceptable: z <- x + y #Not recommended: z<-x+y # (no space) z<- x+y z<-x +y #Acceptable: a <- matrix(c(1:100), nrow = 5) #Not recommended: a <- matrix(c(1:100),nrow = 5) # (no space after comma) a <- matrix( c(1:100), nrow = 5 ) # (extra space after and before parentheses) a<-matrix(c(1:100), nrow = 5) # (no space around unary operator <- ) #Acceptable: # This is a comment #Not recommended: #This is a comment #    This is a comment (more than one space after #) ## This is a comment (multiple #) ###    This is a comment (multiple # and more than one space) #Acceptable: x <- (z + y) #Not recommended: x <- ( z + y ) # (unnecessary space) x <- (z + y ) x <- ( z + y) #Acceptible if (x > 2) {   print(x) } # Not recommended if(x > 2){   print(x) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"other-notes","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Other notes","title":"Contribution","text":"Maximum line length 80 character Use explicit returns Use explicit tags documentation (e.g., @title, @description, …)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"getting-the-code","dir":"Articles","previous_headings":"","what":"Getting the code","title":"Testing the CRE Package","text":"test package, please install package system (R (>= 3.5.0)). can install package following one approaches: Directly GitHub CRAN (recommended) Source Forked Repository (recommended)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-directly-from-github","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package directly from Github","title":"Testing the CRE Package","text":"project, follow successful Git Branching Model. result, develop branch updated branch developers. Use devtools::install_github install package. specify ref, install master (main) branch. Try ?CRE. open package description page help tab (assuming using RStudio).","code":"library(devtools) try(detach(\"package:CRE\", unload = TRUE), silent = TRUE) # if already you have the package, detach and unload it, to have a new install.  install_github(\"NSAPH-Software/CRE\", ref=\"develop\") library(CRE)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-cran","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from CRAN","title":"Testing the CRE Package","text":"Installing package CRAN developing purposes recommended. probably, version CRAN latest version. [Complete section submitting package CRAN]","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-the-source","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from the source","title":"Testing the CRE Package","text":"order install package source, need download source code computer install source. steps: Go package Github repository drop-menu change branch develop. click Code tab click Download Zip tab. Open one files using RStudio, change project directory project directory (Session > Set Working Directory > Project Directory). Load devtools library load CRE.","code":"library(devtools) load_all() ?CRE"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"forking-the-package","dir":"Articles","previous_headings":"Getting the code","what":"Forking the package","title":"Testing the CRE Package","text":"Forking package Github account best option planning installing, testing, modifying, contributing project. Go package Github repository click Fork button top right corner. forking package, Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): Now, can modify codebase track modification. Navigate package folder Install package following Installing package source steps. good idea create new branch work codebase. Read successful Git Branching Model branching convention.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"testing-the-package","dir":"Articles","previous_headings":"","what":"Testing the Package","title":"Testing the CRE Package","text":"Run following command test package.","code":"library(CRE)  # Generate sample data set.seed(1358) dataset <- generate_cre_dataset(n = 1000,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method = \"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\",                       offset = NULL)  hyper_params <- list(intervention_vars = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.01,                      t_corr = 1,                      t_pvalue = 0.05,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 10,                      subsample = 0.5)  # linreg CATE estimation with aipw ITE estimation cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            FASRC Daniela Maria Garcia. Author. Riccardo Cadei. Author. Kwonsang Lee. Author. Falco Joannes Bargagli Stoffi. Author.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Khoshnevis N, Garcia D, Cadei R, Lee K, Bargagli Stoffi F (2023). CRE: Interpretable Discovery Inference Heterogeneous Treatment Effects. R package version 0.2.5.9000, https://github.com/NSAPH-Software/CRE.","code":"@Manual{,   title = {CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},   author = {Naeem Khoshnevis and Daniela Maria Garcia and Riccardo Cadei and Kwonsang Lee and Falco Joannes {Bargagli Stoffi}},   year = {2023},   note = {R package version 0.2.5.9000},   url = {https://github.com/NSAPH-Software/CRE}, }"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"Identifying subgroups study population treatment exposure notably larger smaller effect outcome compared population average crucial social health sciences. estimating conditional average treatment effect (CATE) given pre-specified set covariates common approach, allows estimating causal effects subgroups specified priori researchers. package implements recently developed Causal Rule Ensemble (CRE) algorithm, flexible stable method interpretable discovery estimation heterogeneous treatment effects terms decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"package-details","dir":"","previous_headings":"","what":"Package Details","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"cre() function executes entire CRE algorithm utilizing data set input parameters user provides. divides study data discovery inference subsamples, generating filtering decision rules discovery subsample explain heterogeneity treatment effects. cre() uses inference subsample estimate linear decomposition Conditional Average Treatment Effect (CATE) terms just discovered decision rules, normal equations model ITE estimates. steps, cre() agnostic concerning method used ITE estimation. estimate Individual Treatment Effects (ITE), users can choose various methodologies vary terms speed accuracy. Several ITE estimators internally implemented (.e. AIPW, Causal Forest, Bayesian Causal Forest, Causal BART, S-Learner, T-Learner, X-Learner), customized ITE estimation can also provided input. results collected S3 object containing: number decision rules extracted step, CATE decomposition estimates corresponding uncertainty quantification, list selected parameters, predicted ITEs.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"applications","dir":"","previous_headings":"","what":"Applications","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"CRE package can used diverse theoretical practical applications long working binary exposure treatment level. example, can used education determine teaching methods effective specific student subgroups. marketing, can identify effective strategies different consumer subgroups. healthcare, can used identify patient subgroups benefit particular treatment intervention. can also used studies personalized medicine subgroup patients responds differently provided medicine. public health, CRE method applied discover subgroups vulnerable (resilient) causal effects long-term exposure air pollution mortality.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The CRE package — CRE-package","title":"The CRE package — CRE-package","text":"health social sciences, critically important identify subgroups study population treatment notable heterogeneity causal effects respect average treatment effect. Data-driven discovery heterogeneous treatment effects (HTE) via decision tree methods proposed task. Despite high interpretability, single-tree discovery HTE tends highly unstable find oversimplified representation treatment heterogeneity. accommodate shortcomings, propose Causal Rule Ensemble (CRE), new method discover heterogeneous subgroups ensemble--trees approach. CRE following features: provides interpretable representation HTE; 2) allows extensive exploration complex heterogeneity patterns; 3) guarantees high stability discovery. discovered subgroups defined terms interpretable decision rules, develop general two-stage approach subgroup-specific conditional causal effects estimation, providing theoretical guarantees.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The CRE package — CRE-package","text":"Bargagli-Stoffi, F. J., Cadei, R., Lee, K. Dominici, F. (2023). Causal rule ensemble: Interpretable Discovery Inference Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The CRE package — CRE-package","text":"Naeem Khoshnevis Daniela Maria Garcia Riccardo Cadei Kwonsang Lee Falco Joannes Bargagli Stoffi","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Sensitivity Analysis — analyze_sensitivity","title":"Sensitivity Analysis — analyze_sensitivity","text":"Method analyzing sensitivity estimates causal rule-specific treatment effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sensitivity Analysis — analyze_sensitivity","text":"","code":"analyze_sensitivity(ite_std, rules_matrix_std)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sensitivity Analysis — analyze_sensitivity","text":"ite_std standardized ITE rules_matrix_std standardized causal rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sensitivity Analysis — analyze_sensitivity","text":"list containing results sensitivity analysis","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cre object — autoplot.cre","title":"A helper function for cre object — autoplot.cre","text":"helper function plot cre object using ggplot2 package.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cre object — autoplot.cre","text":"","code":"# S3 method for cre autoplot(object, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cre object — autoplot.cre","text":"object cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cre object — autoplot.cre","text":"Returns ggplot object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_hyper_params","title":"Check input parameters — check_hyper_params","text":"Checks consistency input (hyper) parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_hyper_params","text":"","code":"check_hyper_params(X_names, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_hyper_params","text":"X_names observed covariates names. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_hyper_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_input","title":"Check input parameters — check_input","text":"Checks input parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_input","text":"","code":"check_input(y, z, X, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_input","text":"y observed response vector. z treatment vector. X covariate matrix. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_input","text":"list params might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input data — check_input_data","title":"Check input data — check_input_data","text":"Conducts sanity checks input data.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input data — check_input_data","text":"","code":"check_input_data(y, z, X, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input data — check_input_data","text":"y observed response vector. z treatment vector. X features matrix. ite estimated ITE vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input data — check_input_data","text":"number data samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check method-related parameters — check_method_params","title":"Check method-related parameters — check_method_params","text":"Checks method-related parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check method-related parameters — check_method_params","text":"","code":"check_method_params(y, ite, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check method-related parameters — check_method_params","text":"y observed response vector. ite estimated ITE vector. params list parameters required run method functions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check method-related parameters — check_method_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Causal rule ensemble — cre","title":"Causal rule ensemble — cre","text":"Performs Causal Rule Ensemble data set response variable, treatment variable, various features.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Causal rule ensemble — cre","text":"","code":"cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Causal rule ensemble — cre","text":"y observed response vector. z treatment vector. X covariate matrix (data frame). provided numerical values. method_params list parameters define models used, including: Parameters Honest Splitting ratio_dis: ratio data delegated rules discovery (default: 0.5). Parameters Discovery Inference ite_method: method ITE (pseudo-outcome) estimation (default: \"aipw\", options: \"aipw\" Augmented Inverse Probability Weighting, \"cf\" Causal Forest, \"bart\" Causal Bayesian Additive Regression Trees, \"slearner\" S-Learner, \"tlearner\" T-Learner, \"xlearner\" X-Learner, \"tpoisson\" T-Learner Poisson regression). learner_ps: model propensity score estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"bart\", \"cf\" ITE estimators). learner_y: model outcome estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"slearner\", \"tlearner\" \"xlearner\" ITE estimators). hyper_params list hyper parameters fine-tune method, including: General hyper parameters intervention_vars: Array intervention-able covariates names used Rules Generation. Empty null array means covariates considered intervention-able (default: NULL). ntrees: number decision trees random forest (default: 20). node_size: Minimum size trees' terminal nodes (default: 20). max_rules: Maximum number generated candidates rules (default: 50). max_depth: Maximum rules length (default: 3). t_decay: decay threshold rules pruning. Higher values carry aggressive pruning (default: 0.025). t_ext: threshold truncate generic specific (extreme) rules (default: 0.01, range: [0, 0.5)). t_corr: threshold define correlated rules (default: 1, range: [0,+inf)). stability_selection: Method stability selection selecting rules. \"vanilla\" stability selection, \"error_control\" stability selection error control  \"\" stability selection (default: \"vanilla\"). B: Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). subsample: Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). Method specific hyper parameters offset: Name covariate use offset (.e., \"x1\") T-Poisson ITE estimation. Use NULL offset used (default: NULL). cutoff:  Threshold (percentage) defining minimum cutoff value stability scores Stability Selection (default: 0.9). pfer: Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). ite estimated ITE vector. given ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Causal rule ensemble — cre","text":"S3 object composed : M number Decision Rules extracted step, CATE data.frame Conditional Average Treatment Effect decomposition estimates corresponding uncertainty quantification, method_params list method parameters, hyper_params list hyper parameters, rules list rules (implicit form) decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Causal rule ensemble — cre","text":"intervention_vars provided, important note individual treatment effect still computed using covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Causal rule ensemble — cre","text":"","code":"# \\donttest{ set.seed(123) dataset <- generate_cre_dataset(n = 400,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = NULL,                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 20,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) #> Loading required package: nnls #> Registered S3 method overwritten by 'randomForest': #>   method      from #>   plot.margin RRF  # }"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":null,"dir":"Reference","previous_headings":"","what":"The Causal Rule Ensemble Crossfit — cre_crossfit","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"Performs Causal Rule Ensemble twice dataset response variable, treatment variable, various features, swapping discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"cre_crossfit(   y,   z,   X,   ite_method_dis,   include_ps_dis = NA,   ps_method_dis = \"SL.xgboost\",   or_method_dis = NA,   ite_method_inf,   include_ps_inf = NA,   ps_method_inf = \"SL.xgboost\",   or_method_inf = NA,   ntrees_rf,   ntrees_gbm,   min_nodes,   max_nodes,   t,   q,   rules_method = NA,   include_offset = FALSE,   offset_name = NA,   cate_method = \"DRLearner\",   cate_SL_library = \"SL.xgboost\",   filter_cate = FALSE )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"y observed response vector z treatment vector X covariate matrix ite_method_dis method estimate discovery sample ITE include_ps_dis whether include propensity score estimate covariate discovery ITE estimation, considered BART, XBART, CF ps_method_dis estimation model propensity score discovery subsample or_method_dis estimation model outcome regressions estimate_ite_aipw discovery subsample ite_method_inf method estimate inference sample ITE include_ps_inf whether include propensity score estimate covariate inference ITE estimation, considered BART, XBART, CF ps_method_inf estimation model propensity score inference subsample or_method_inf estimation model outcome regressions estimate_ite_aipw inference subsample ntrees_rf number decision trees randomForest ntrees_gbm number decision trees gradient boosting min_nodes minimum size trees' terminal nodes max_nodes maximum size trees' terminal nodes t common support used generating causal rules matrix q selection threshold used selecting causal rules rules_method method selecting causal rules binary outcomes include_offset whether include offset estimating ITE, poisson offset_name name offset, included cate_method method estimate CATE values cate_SL_library library used cate_method set DRLearner filter_cate whether filter rules p-value <= 0.05","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"S3 object containing matrix averaged Conditional Average Treatment Effect estimates two CRE iterations","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  cre_cross_results <- cre_crossfit(y = dataset[[\"y\"]], z = dataset[[\"z\"]],                                   X = as.data.frame(dataset[[\"X\"]]),                                   ite_method_dis = \"bart\",                                   include_ps_dis = TRUE,                                   ite_method_inf = \"bart\",                                   include_ps_inf = TRUE,                                   ntrees_rf = 100, ntrees_gbm = 50,                                   min_nodes = 20, max_nodes = 5,                                   t = 0.025, q = 0.8) #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> Conducting Discovery Subsample Analysis 1 #> 411 rules (length<=15) were extracted from the first 100 trees. #> 150 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 1 #> Conducting Discovery Subsample Analysis 2 #> 493 rules (length<=15) were extracted from the first 100 trees. #> 100 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 2"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"Discard rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"","code":"discard_anomalous_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"Discard redundant correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"","code":"discard_correlated_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover causal decision rules — discover_causal_rules","title":"Discover causal decision rules — discover_causal_rules","text":"Discovers Causal Decision Rules randomization-based tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover causal decision rules — discover_causal_rules","text":"","code":"discover_causal_rules(   rules_matrix,   rules_list,   ite,   stability_selection,   cutoff,   pfer,   penalty_rl )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover causal decision rules — discover_causal_rules","text":"rules_matrix causal rules matrix. rules_list vector causal rules. ite estimated ITE. stability_selection Whether using stability selection select causal rules. cutoff Threshold (percentage) defining minimum cutoff value stability scores. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). penalty_rl Order penalty rules length LASSO Causal Rules Discovery (.e. 0: penalty, 1: rules_length, 2: rules_length^2)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover causal decision rules — discover_causal_rules","text":"List Causal Decision Rules discovered","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover rules — discover_rules","title":"Discover rules — discover_rules","text":"Discover minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover rules — discover_rules","text":"","code":"discover_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover rules — discover_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover rules — discover_rules","text":"minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Conditional Average Treatment Effect — estimate_cate","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"Estimates Conditional Average Treatment Effect (CATE) linearly modeling Individual Treatment Effect set rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"","code":"estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"rules_matrix rules matrix. rules_explicit list select rules terms covariate names. ite estimated ITEs. B number bootstrap samples uncertainty quantification estimation. subsample bootstrap ratio subsample uncertainty quantification estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"list 2 elements: summary: data frame summarizing CATE linear decomposition: Rule: rule name, Estimate: linear contribution CATE, CI_lower: lower bound 95% confidence interval estimate, CI_upper: upper bound 95% confidence interval estimate, P_Value: p-value (Z-test). model: linear model CATE-ATE estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"Estimates Individual Treatment Effect given response vector, treatment vector, covariate matrix, desired algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"","code":"estimate_ite(y, z, X, ite_method, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"y observed response vector. z observed treatment vector. X covariate matrix. ite_method method estimating Individual Treatment Effect. methods requires additional parameters. parameters mentioned indented blocks method definitions provided end parameters list. \"slearner\": S-Learner. - learner_y \"tlearner\": T-Learner. learner_y \"tpoisson\": T-Poisson. offset \"xlearner\": X-Learner. learner_y \"aipw\": Augmented Inverse Probability Weighting. learner_ps  learner_y \"bart\": Bayesian Additive Regression Trees. learner_ps \"cf\": Causal Forest. learner_ps ... Additional parameters passed different models.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"list ITE estimates.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"additional-parameters","dir":"Reference","previous_headings":"","what":"Additional parameters","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"learner_ps: estimation method propensity score. includes libraries SuperLearner package. learner_y: estimation model outcome. includes libraries SuperLearner package. offset: Name covariate use offset (.e. \"x1\") Poisson ITE Estimation. NULL offset used.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"Estimates Individual Treatment Effect using Augmented Inverse Probability Weighting given response vector, treatment vector, features matrix, estimation model propensity score estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"","code":"estimate_ite_aipw(y, z, X, learner_ps = \"SL.xgboost\", learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"y observed response vector. z treatment vector. X features matrix. learner_ps estimation model propensity score. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"Estimates Individual Treatment Effect using Bayesian Additive Regression Trees given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"","code":"estimate_ite_bart(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"y observed response vector. z treatment vector. X features matrix. learner_ps Method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"number samples number burn set default equal 500.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"Estimates Individual Treatment Effect using Bayesian Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"","code":"estimate_ite_bcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"y observed response vector. z treatment vector. X features matrix. ps_method method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"Estimates Individual Treatment Effect using Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"","code":"estimate_ite_cf(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"y observed response vector. z treatment vector. X features matrix. learner_ps method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"Estimates Individual Treatment Effect (ITE) using Inverse Propensity Weighting (IPW) given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"","code":"estimate_ite_ipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"Method estimating Individual Treatment Effect using Outcome Regression given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"estimate_ite_or(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"y observed response vector z treatment vector X features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  # Initialize parameters y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- as.data.frame(dataset[[\"X\"]])  ite_list <- estimate_ite_or(y, z, X) #> *****Into main of wbart #> *****Data: #> data:n,p,np: 388, 10, 612 #> y1,yn: -0.694294, 1.362455 #> x1,x[n*p]: 1.000000, 1.000000 #> xp1,xp[np*p]: 0.000000, 0.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.118817,3.000000,0.250422 #> *****sigma: 1.133838 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000 #> *****Into main of wbart #> *****Data: #> data:n,p,np: 612, 10, 388 #> y1,yn: -0.485955, 0.857738 #> x1,x[n*p]: 0.000000, 0.000000 #> xp1,xp[np*p]: 1.000000, 1.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.139249,3.000000,0.168737 #> *****sigma: 0.930723 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"Estimates Individual Treatment Effect (ITE) using Outcome Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"","code":"estimate_ite_oreg(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"y observed response vector. z treatment vector. X features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"Estimates Individual Treatment Effect using Poisson Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"","code":"estimate_ite_poisson(y, z, X, X_names, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"y observed response vector. z treatment vector. X features matrix. X_names vector includes names covariates. offset name covariate use offset (.e. 'x1') model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"Estimates Individual Treatment Effect using Stabilized Inverse Propensity Weighting given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"","code":"estimate_ite_sipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"Estimates Individual Treatment Effect using S-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"","code":"estimate_ite_slearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"Estimates Individual Treatment Effect using T-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"","code":"estimate_ite_tlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"Estimates Individual Treatment Effect using Poisson regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"","code":"estimate_ite_tpoisson(y, z, X, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"y observed response vector. z treatment vector. X features matrix. offset name covariate use offset (.e. “x1”) model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"","code":"estimate_ite_xbart(y, z, X, include_ps, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"y observed response vector z treatment vector X features matrix include_ps whether include propensity score estimate covariate ITE estimation ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Causal Forest given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"","code":"estimate_ite_xbcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"y observed response vector z treatment vector X features matrix ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"Estimates Individual Treatment Effect using X-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"","code":"estimate_ite_xlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the propensity score — estimate_ps","title":"Estimate the propensity score — estimate_ps","text":"Estimates Propensity Score given treatment vector features data frame.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the propensity score — estimate_ps","text":"","code":"estimate_ps(z, X, ps_method = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the propensity score — estimate_ps","text":"z treatment vector. X features data frame. ps_method estimation model propensity score (default: SL.xgboost).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the propensity score — estimate_ps","text":"vector propensity score estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":null,"dir":"Reference","previous_headings":"","what":"Discovery (performance) evaluation — evaluate","title":"Discovery (performance) evaluation — evaluate","text":"Computes different metrics discovery evaluation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discovery (performance) evaluation — evaluate","text":"","code":"evaluate(ground_truth, prediction)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discovery (performance) evaluation — evaluate","text":"ground_truth List true (CDR/EM) values. prediction List predicted (CDR/EM) values.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discovery (performance) evaluation — evaluate","text":"Intersection union, precision, recall.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract effect modifiers — extract_effect_modifiers","title":"Extract effect modifiers — extract_effect_modifiers","text":"Extracts effect modifiers list (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract effect modifiers — extract_effect_modifiers","text":"","code":"extract_effect_modifiers(rules_list, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract effect modifiers — extract_effect_modifiers","text":"rules_list list (causal) decision rules. X_names list covariate names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract effect modifiers — extract_effect_modifiers","text":"list effect modifiers.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract (causal) decision rules — extract_rules","title":"Extract (causal) decision rules — extract_rules","text":"Extracts causal rules random forest gradient boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract (causal) decision rules — extract_rules","text":"","code":"extract_rules(treelist, X, max_depth, digits = 2)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract (causal) decision rules — extract_rules","text":"treelist list decision trees. X Features matrix. max_depth number top levels tree considered. digits Number digits rounding decision rules extract conditions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract (causal) decision rules — extract_rules","text":"vector (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter correlated rules — filter_correlated_rules","title":"Filter correlated rules — filter_correlated_rules","text":"Discards highly correlated rules (.e., \\(Cov(rule_1,rule_2) > t_{corr}\\)).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter correlated rules — filter_correlated_rules","text":"","code":"filter_correlated_rules(rules_matrix, rules_list, t_corr)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter correlated rules — filter_correlated_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_corr threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter correlated rules — filter_correlated_rules","text":"rules matrix without highly correlated rules (columns).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter extreme decision rules — filter_extreme_rules","title":"Filter extreme decision rules — filter_extreme_rules","text":"Discards rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter extreme decision rules — filter_extreme_rules","text":"","code":"filter_extreme_rules(rules_matrix, rules_list, t_ext)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_ext threshold [0, 0.5) range truncate generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules matrix without rare/common rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"Filters irrelevant decision rules. irrelevant rules interpreted error increase removing variable-value pair decision rules (see “Interpreting tree ensembles inTrees package” Houtao Deng, 2019).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"","code":"filter_irrelevant_rules(rules, X, ite, t_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"rules list rules. X features matrix. ite estimated ITE. t_decay decay threshold rules pruning.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"list “relevant” rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate causal rules — generate_causal_rules","title":"Generate causal rules — generate_causal_rules","text":"Generates causal decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate causal rules — generate_causal_rules","text":"","code":"generate_causal_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate causal rules — generate_causal_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate causal rules — generate_causal_rules","text":"vector causal rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate CRE synthetic data — generate_cre_dataset","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Generates synthetic data sets run simulation causal inference experiments composed outcome vector (y), treatment vector (z), covariates matrix (X), unobserved individual treatment effects vector (ite). arguments specify data set characteristic, including number individuals (n), number covariates (p), correlation within covariates (rho),  number decision rules (n_rules) decomposing Conditional Average Treatment Effect (CATE), treatment effect magnitude (effect_size), confounding mechanism (confounding), whether covariates outcomes binary continuous (binary_covariates, binary_outcome).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"generate_cre_dataset(   n = 1000,   rho = 0,   n_rules = 2,   p = 10,   effect_size = 2,   binary_covariates = TRUE,   binary_outcome = TRUE,   confounding = \"no\" )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate CRE synthetic data — generate_cre_dataset","text":"n integer number represents number observations. Non-integer values converted integer number. rho positive double number represents correlation within covariates (default: 0, range: [0,1)). n_rules number causal rules (default: 2, range: {1,2,3,4}). p number covariates (default: 10). effect_size treatment effect size magnitude (default: 2, range: \\(\\geq\\)0). binary_covariates Whether use binary continuous covariates (default: TRUE). binary_outcome Whether use binary continuous outcomes (default: TRUE). confounding continuous outcome, add confounding variables: \"lin\" linear confounding, \"nonlin\" non-linear confounding, \"\" confounding (default).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate CRE synthetic data — generate_cre_dataset","text":"list, representing generated synthetic data set, containing: y outcome vector, z treatment vector, X covariates matrix, ite individual treatment vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate CRE synthetic data — generate_cre_dataset","text":"covariates matrix generated specified correlation among individuals, covariate sampled either Bernoulli(0.5) binary, Gaussian(0,1) continuous. treatment vector sampled Bernoulli(\\(\\frac{1}{1+ \\exp(1-x_1+x_2-x_3)}\\)), enforcing treatment assignment probabilities function observed covariates. potential outcomes (\\(y(0)\\) \\(y(1)\\)) sampled Bernoulli binary, Gaussian (standard deviation equal 1) continuous. mean equal confounding term (null, linear non-linear always null binary outcome) plus 1-4 decision rules weighted treatment effect magnitude. two potential outcomes characterizes CATE (unobserved individual treatment effects vector) sum different additive contributions decision rules considered (plus intercept). final expression CATE depends treatment effect magnitude number decision rules considered. 4 decision rules : Rule 1: \\(1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x})\\) Rule 2: \\(1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})\\) Rule 3: \\(1\\{x_4 \\leq 0.5\\}(\\textbf{x})\\) Rule 4: \\(1\\{x_5 \\leq 0.5; x_7 > 0.5; x_8 \\leq 0.5\\}(\\textbf{x})\\) corresponding additive average treatment effect (AATE) equal : Rule 1: \\(-\\) effect_size, Rule 2: \\(+\\) effect_size, Rule 3: \\(- 0.5 \\cdot\\) effect_size, Rule 4: \\(+ 2 \\cdot\\) effect_size. example, setting effect_size=4 n_rules=2: $$\\text{CATE}(\\textbf{x}) = -4 \\cdot 1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x}) + 4 \\cdot 1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})$$ final outcome vector y finally computed combining potential outcomes according treatment assignment.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Set covariates domain (binary_covariates) outcome domain (binary_outcome) according experiment interest. Increase complexity heterogeneity discovery: decreasing sample size (n), adding correlation among covariates (rho), increasing number rules (n_rules), increasing number covariates (p), decreasing absolute value causal effect (effect_size), adding linear -linear confounders (confounding).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"set.seed(123) dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary_covariates = TRUE,                                 binary_outcome = TRUE, confounding = \"no\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules — generate_rules","title":"Generate rules — generate_rules","text":"Generates list rules characterizing heterogeneity Conditional Average Treatment Effect (CATE) tree-based methods (.e., random forest).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules — generate_rules","text":"","code":"generate_rules(X, ite, ntrees, node_size, max_rules, max_depth)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules — generate_rules","text":"X covariate matrix. ite vector estimated ITE. ntrees number decision trees random forest algorithm. node_size Minimum size trees' terminal nodes. max_rules Maximum number candidate decision rules. max_depth Maximum rules length.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules — generate_rules","text":"list rules (names).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules matrix — generate_rules_matrix","title":"Generate rules matrix — generate_rules_matrix","text":"Generates rules matrix feature covariate matrix vector rules. number rows rules_matrix equal number samples X, number columns equal number rules rules_list. element rules_matrix corresponds specific data sample rule. data sample satisfies rule, corresponding element rules_matrix set 1. Otherwise, element set 0.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules matrix — generate_rules_matrix","text":"","code":"generate_rules_matrix(X, rules_list)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules matrix — generate_rules_matrix","text":"X Features matrix. rules_list vector rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules matrix — generate_rules_matrix","text":"causal rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Logger settings — get_logger","title":"Get Logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":null,"dir":"Reference","previous_headings":"","what":"Honest splitting — honest_splitting","title":"Honest splitting — honest_splitting","text":"Splits data discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Honest splitting — honest_splitting","text":"","code":"honest_splitting(y, z, X, ratio_dis, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Honest splitting — honest_splitting","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis double number indicating ratio data delegated discovery sub-sample. ite vector estimated ITE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Honest splitting — honest_splitting","text":"list containing discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret rules — interpret_rules","title":"Interpret rules — interpret_rules","text":"Replaces column numbers rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret rules — interpret_rules","text":"","code":"interpret_rules(rules, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret rules — interpret_rules","text":"rules vector rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret rules — interpret_rules","text":"list explicit (human-readable) rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret select rules — interpret_select_rules","title":"Interpret select rules — interpret_select_rules","text":"Replaces column numbers select rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret select rules — interpret_select_rules","text":"","code":"interpret_select_rules(select_rules_dis, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret select rules — interpret_select_rules","text":"select_rules_dis vector select causal rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret select rules — interpret_select_rules","text":"vector select causal rules interpretable.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"","code":"lasso_rules_filter(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q Number (unique) selected rules per subsample stability selection. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cre class — plot.cre","title":"Extend generic plot functions for cre class — plot.cre","text":"wrapper function extend generic plot functions cre class.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cre class — plot.cre","text":"","code":"# S3 method for cre plot(x, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cre class — plot.cre","text":"x cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cre class — plot.cre","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict individual treatment effect via causal rule ensemble — predict.cre","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"Predicts individual treatment effect via causal rule ensemble algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"","code":"# S3 method for cre predict(object, X, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"object cre object running CRE function. X covariate matrix (data.frame) ... Additional arguments passed customize prediction.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"array estimated Individual Treatment Effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for the CRE object — print.cre","title":"Extend print function for the CRE object — print.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for the CRE object — print.cre","text":"","code":"# S3 method for cre print(x, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for the CRE object — print.cre","text":"x cre object running CRE function. verbose Set level results description details: 0 results summary, 1 results parameters summary, 2 results parameters rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for the CRE object — print.cre","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune (Causal) Decision Rules — prune_rules","title":"Prune (Causal) Decision Rules — prune_rules","text":"Method pruning (Causal) Decision Rules extracted Random Forest Gradient Boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune (Causal) Decision Rules — prune_rules","text":"","code":"prune_rules(rules, X, ite_std, max_decay, type_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune (Causal) Decision Rules — prune_rules","text":"rules list decision rules. X features matrix. ite_std standardized ITE. max_decay Decay Threshold pruning rules. type_decay Decay Type pruning rules (1: relative error; 2: error).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune (Causal) Decision Rules — prune_rules","text":"list (Causal) Decision Rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select Causal Rules — select_causal_rules","title":"Select Causal Rules — select_causal_rules","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select Causal Rules — select_causal_rules","text":"","code":"select_causal_rules(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select Causal Rules — select_causal_rules","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q selection threshold used selecting causal rules. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select Causal Rules — select_causal_rules","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select rules — select_rules","title":"Select rules — select_rules","text":"Given set rules, selects minimal set linearly decomposing Conditional Average Treatment Effect (CATE) LASSO (optionally Stability Selection).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select rules — select_rules","text":"","code":"select_rules(rules_matrix, rules, ite, stability_selection, cutoff, pfer, B)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select rules — select_rules","text":"rules_matrix rules matrix. rules list rules. ite estimated ITE. stability_selection Stability selection method. cutoff Threshold (percentage) defining minimum cutoff value stability scores. stability selection. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). stability selection. B Number bootstrap samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select rules — select_rules","text":"minimal set rules linearly decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Logger settings — set_logger","title":"Set Logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"CRE.log\", logger_level = \"INFO\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: CRE.log) logger_level log level. log level set, log levels also activated (implemented). Available levels include: TRACE: Provides verbose detailed logging, including steps taken achieve result, often used debugging. Activating TRACE also enable DEBUG, INFO, SUCCESS, WARN, ERROR, FATAL logs. DEBUG: Provides detailed logging flow application, used mostly developers understand potential issues. Activating DEBUG also enable INFO, SUCCESS, WARN, ERROR, FATAL logs. INFO (Default): Standard messages inform user normal operation system. Activating INFO also enable SUCCESS, WARN, ERROR, FATAL logs. SUCCESS: Messages indicating successful completion particular operation task. Activating SUCCESS also enable WARN, ERROR, FATAL logs. WARN: Warning messages events might cause problems future, yet errors. Activating WARN also enable ERROR FATAL logs. ERROR: Reports error due system may able achieve functionality, application halt. Activating ERROR also enable FATAL logs. FATAL: Reports severe error events presumably lead application abort.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set Logger settings — set_logger","text":"Log levels specified developers initial implementation. Future developers contributors can leverage log levels better capture document application's processes events.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Logger settings — set_logger","text":"","code":"set_logger(\"Debug\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Data — split_data","title":"Split Data — split_data","text":"Splits data discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Data — split_data","text":"","code":"split_data(y, z, X, ratio_dis)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Data — split_data","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis ratio data delegated discovery subsample","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Data — split_data","text":"list containing discovery inference subsamples","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardize Rules Matrix — standardize_rules_matrix","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardize (.e. mean=0 stdev=1) rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"","code":"standardize_rules_matrix(rules_matrix)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"rules_matrix rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardized rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of CRE object — summary.cre","title":"Print summary of CRE object — summary.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of CRE object — summary.cre","text":"","code":"# S3 method for cre summary(object, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of CRE object — summary.cre","text":"object cre object running CRE function. verbose Set level results description details: results summary 0, results+parameters summary 1, results+parameters+rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of CRE object — summary.cre","text":"summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":null,"dir":"Reference","previous_headings":"","what":"Take 1 — take1","title":"Take 1 — take1","text":"Generate list indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take 1 — take1","text":"","code":"take1(len)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take 1 — take1","text":"len length value","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take 1 — take1","text":"vector indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take 1 — take1","text":"","code":"ind <- CRE:::take1(10)"},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-025-2023-12-6","dir":"Changelog","previous_headings":"","what":"CRE 0.2.5 (2023-12-6)","title":"CRE 0.2.5 (2023-12-6)","text":"CRAN release: 2023-12-06","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-5","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.5 (2023-12-6)","text":"Add (vanilla) Stability Selection (without Error Control). max_rules hyper parameters max rules filtering. Uncertainty Quantification estimation bootstrapping. B hyper-parameter, subsample hyper-parameter. rules(implicit form) cre() function return. predict() function ITE estimation via CRE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-5","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.5 (2023-12-6)","text":"Type stability_selection binary -> string (‘’,‘vanilla’,‘error_control’). Unify ntrees_gbm hyper-parameter ntrees_gbm hyper-parameter ntrees hyper-parameter. rules generation retrieve decision rules also internal nodes, just terminal nodes. ite_method_dis, ite_method_inf method-parameter -> ite_method. ps_method_dis, ps_method_inf method-parameter -> learner_ps. oreg_method_dis, oreg_method_inf method-parameter -> learner_y.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-5","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.5 (2023-12-6)","text":"max_nodes hyper-parameter. Remove rules generation Generalized Boosted Regression. replace hyper-parameter. penalty_rl hyper-parameter. t_pvalue hyper-parameter. ite_pred cre() function return.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.5 (2023-12-6)","text":"Error saving covariates name CRE result using intervention_vars.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-024-2023-6-14","dir":"Changelog","previous_headings":"","what":"CRE 0.2.4 (2023-6-14)","title":"CRE 0.2.4 (2023-6-14)","text":"CRAN release: 2023-06-14","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-4","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.4 (2023-6-14)","text":"Method paper description updated.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-023-2023-4-27","dir":"Changelog","previous_headings":"","what":"CRE 0.2.3 (2023-4-27)","title":"CRE 0.2.3 (2023-4-27)","text":"CRAN release: 2023-04-27","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-3","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.3 (2023-4-27)","text":"Bayesian Causal Forest (bcf) ITE estimator supported.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-022-2023-4-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.2 (2023-4-17)","title":"CRE 0.2.2 (2023-4-17)","text":"CRAN release: 2023-04-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-2","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.2 (2023-4-17)","text":"Fixed failing unit tests specific operating systems.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-021-2023-3-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.1 (2023-3-17)","title":"CRE 0.2.1 (2023-3-17)","text":"CRAN release: 2023-03-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.1 (2023-3-17)","text":"Replace BATE ATE CATE Linear Decomposition. Update plot() function (remove ATE, old BATE, explicit AATEs).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.1 (2023-3-17)","text":"Code Conduct.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.1 (2023-3-17)","text":"Causal Tree benchmark functional tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.1 (2023-3-17)","text":"Rank-Deficient Rule Matrix Issue (redundant rules). Intervention Variables Filtering (ordered filtering).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-020-2023-1-19","dir":"Changelog","previous_headings":"","what":"CRE 0.2.0 (2023-1-19)","title":"CRE 0.2.0 (2023-1-19)","text":"CRAN release: 2023-01-19","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.0 (2023-1-19)","text":"offset method-parameter -> hyper-parameter estimate_ite_poisson function -> estimate_ite_tpoisson max_dacay hyper-parameter -> t_decay. interpret_select_rules function -> interpret_rules. generate_causal_rules function -> discover_rules. discover_causal_rules function ->select_rules. offset_name method parameter -> offset. Hyper method parameters required arguments cre. cre object: added parameters ite estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.0 (2023-1-19)","text":"Synthetic data set 1 3 rules (generate_cre_dataset). S-Learner (slearner) method ITE estimation. T-Learner (tlearner) method ITE estimation. X-Learner (xlearner) method ITE estimation. Rules Selection description summary.cre. verbose parameter summary.cre. ite, additional cre input parameter use personalized ite estimations. Default values hyper parameters. Default values method parameters. Simulation experiments estimation (estimation.R). Simulation experiments discovery (discovery.R). extract_effect_modifiers function (utility performance evaluation). evaluate function discovery evaluation. confounding parameter generate_cre_dataset set confounding type. ite_pred model CRE results. binary_covariates parameter generate_cre_dataset set covariates domain.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.0 (2023-1-19)","text":"include_ps_inf method-parameter. include_ps_dis method-parameter. oreg method ITE estimation. ipw method ITE estimation. sipw method ITE estimation. ITE standard deviation estimation. type_decay hyper-parameter. Keep linreg CATE estimation (remove cate_method cate_SL_library parameters). method_params hyper_params additional parameters summary.cre. ite standardization Rules Generation. random_state parameter. include_offset method parameter.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.0 (2023-1-19)","text":"Rules Generation Issue (set rules length fix bootstrapping).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-011-2022-10-18","dir":"Changelog","previous_headings":"","what":"CRE 0.1.1 (2022-10-18)","title":"CRE 0.1.1 (2022-10-18)","text":"CRAN release: 2022-10-22","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.1 (2022-10-18)","text":"binary parameter generate_cre_dataset -> binary_outcome . filter_cate hyper-parameter -> t_pvalue. t_anom hyper-parameter -> t_ext. effect_modifier hyper-parameter -> intervention_vars. lasso_rules_filter function -> discover_causal_rules. split_data function -> honest_splitting. prune_rules function -> `filter_irrelevant_rules. discard_correlated_rules function -> filter_correlated_rules. discard_anomalous_rules function -> filter_extreme_rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.1 (2022-10-18)","text":"Weighted LASSO Causal Rules Discovery (penalty_rl hyper-parameter).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-010-2022-10-17","dir":"Changelog","previous_headings":"","what":"CRE 0.1.0 (2022-10-17)","title":"CRE 0.1.0 (2022-10-17)","text":"CRAN release: 2022-10-18","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.0 (2022-10-17)","text":"Update examples tests functions. q hyper-parameter -> cutoff. pfer_val hyper-parameter -> pfer. select_causal_rules function -> lasso_rules_filter. t hyper-parameter -> t_anom. Separate standardization, remove filtering generate_rules_matrix function. summary.cre function describe results. min_nodes hyper-parameter -> node_size (randomForest convention). cre returns S3 object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.0 (2022-10-17)","text":"Examples tests functions. prune_rules function discard un-predictive rules. discard_anomalous_rules function discard anomalous rules (see t_corr hyper-parameter.). discard_correlated_rules function discard correlated rules (see t_anom hyper-parameter). effect_modifiers parameter generate_rules function covariates filtering. generate_causal_rules function. Helper function SuperLearner package propensity score estimation estimate_ite_xyz. Five methods CATE estimation (poisson, DRLearner, bart-baggr, cf-means, linreg) estimate_cate function. (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) method-parameters complement SuperLearner package. cate_method method-parameter select CATE estimation method. filter_cate method-parameter estimation filtering. p parameter (generate_cre_dataset function) set number covariates. replace parameter (generate_rules function) allow bootstrapping. cre.print generic function print cre S3 object results. cre.summary generic functions summarize cre S3 object Results. check_input function isolate input checks. estimate_ite_aipw function augmented inverse propensity weighting. plot.cre generic function plot cre S3 object results. test-cre_functional.R test functionality package. stability_selection function causal rules selection.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-1-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.1.0 (2022-10-17)","text":"estimate_ite_blp function. take1() function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.1.0 (2022-10-17)","text":"Undesired ‘’ Decision Rule Issue. Causal Rule Selected Issue.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.0.1 (2021-10-20)","text":"estimate_cate include two methods estimating CATE values. cre added initial checks binary outcome whether include propensity score ITE estimation. estimate_ite_xyz conduct propensity score estimation using helper function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.0.1 (2021-10-20)","text":"Example generate_cre_dataset. set_logger get_logger. check_input_data function. generate_cre_dataset function generate synthetic data testing package. test-generate_cre_dataset function test. estimate_ps function estimate propensity score. estimate_ite_xbart function generate ITE estimates using accelerated BART. estimate_ite_xbcf function generate ITE estimates using accelerated BCF. analyze_sensitivity function conduct sensitivity analysis unmeasured confounding. cre function perform entire Causal Rule Ensemble method. estimate_cate function generate CATE estimates ITE estimates select rules. estimate_ite function generate ITE estimates using user-specified method (calls estimate_ite_xyz functions). estimate_ite_bart function generate ITE estimates using BART. estimate_ite_bcf function generate ITE estimates using Bayesian Causal Forests. estimate_ite_cf function generate ITE estimates using Causal Forests. estimate_ite_ipw function generate ITE estimates using IPW. estimate_ite_or function generate ITE estimates using Outcome Regression. estimate_ite_sipw function generate ITE estimates using SIPW. extract_rules function extract list causal rules randomForest GBM models. generate_rules function generate causal rule models using randomForest GBM methods. generate_rules_matrix function convert list causal rules matrix. select_causal_rules function apply penalized regression causal rules. select important ones. split_data function split input data discovery inference subsamples. take1 function create subsample indices.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-0-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.0.1 (2021-10-20)","text":"seed argument generate_cre_datase function.","code":""}]
diff --git a/index.md b/index.md
index f93f8c16..29dadee4 100644
--- a/index.md
+++ b/index.md
@@ -2,6 +2,7 @@
 
 <!-- badges: start -->
 [![](http://www.r-pkg.org/badges/version-last-release/CRE)](https://CRAN.R-project.org/package=CRE)
+[![status](https://joss.theoj.org/papers/86a406543801a395248821c08c7ec03d/status.svg)](https://joss.theoj.org/papers/86a406543801a395248821c08c7ec03d)
 [![R-CMD-check](https://github.com/fasrc/CRE/workflows/R-CMD-check/badge.svg)](https://github.com/fasrc/CRE/actions)
 [![codecov](https://codecov.io/gh/NSAPH-Software/CRE/branch/develop/graph/badge.svg?token=UMSVOYRKGA)](https://codecov.io/gh/NSAPH-Software/CRE)
 [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/grand-total/CRE)](http://www.r-pkg.org/pkg/cre)

From 05f108fdce1728ab80820bb9f2a68da8f70e040d Mon Sep 17 00:00:00 2001
From: naeemkh <khoshnevis.naeem@gmail.com>
Date: Fri, 15 Dec 2023 14:18:53 -0500
Subject: [PATCH 3/5] update citation

---
 README.md         | 16 +++++++++++-----
 docs/authors.html | 23 ++++++++++++++---------
 docs/pkgdown.yml  |  2 +-
 docs/search.json  |  2 +-
 inst/CITATION     | 25 +++++++++++++++++++++++++
 5 files changed, 52 insertions(+), 16 deletions(-)
 create mode 100644 inst/CITATION

diff --git a/README.md b/README.md
index c9c8004c..cb9f0c48 100644
--- a/README.md
+++ b/README.md
@@ -226,11 +226,17 @@ Please note that the CRE project is released with a [Contributor Code of Conduct
 
 Causal Rule Ensemble ([methodological paper](https://arxiv.org/abs/2009.09036))
 ```bibtex
-@article{bargagli2023causal,
-  title={{Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects}},
-  author={Bargagli-Stoffi, Falco J and Cadei, Riccardo and Lee, Kwonsang and Dominici, Francesca},
-  journal={arXiv preprint arXiv:2009.09036},
-  year={2023}
+@article{Cadei2023,
+  doi = {10.21105/joss.05587},
+  url = {https://doi.org/10.21105/joss.05587},
+  year = {2023},
+  publisher = {The Open Journal},
+  volume = {8},
+  number = {92},
+  pages = {5587},
+  author = {Riccardo Cadei and Naeem Khoshnevis and Kwonsang Lee and Daniela Maria Garcia and Falco J. Bargagli Stoffi},
+  title = {CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects},
+  journal = {Journal of Open Source Software},
 }
 ```
 
diff --git a/docs/authors.html b/docs/authors.html
index 51325a43..39fc8610 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -83,18 +83,23 @@ <h2>Authors</h2>
 
     <div class="section level2 citation">
       <h2 id="citation">Citation</h2>
-      <p><small class="dont-index">Source: <a href="https://github.com/NSAPH-Software/CRE/blob/HEAD/DESCRIPTION"><code>DESCRIPTION</code></a></small></p>
+      <p><small class="dont-index">Source: <a href="https://github.com/NSAPH-Software/CRE/blob/HEAD/inst/CITATION"><code>inst/CITATION</code></a></small></p>
 
-      <p>Khoshnevis N, Garcia D, Cadei R, Lee K, Bargagli Stoffi F (2023).
-<em>CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects</em>.
-R package version 0.2.5.9000, <a href="https://github.com/NSAPH-Software/CRE">https://github.com/NSAPH-Software/CRE</a>. 
+      <p>Cadei R, Khoshnevis N, Lee K, Garcia D, Stoffi F (2023).
+“CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects.”
+<em>Journal of Open Source Software</em>, <b>8</b>(92), 5587.
+<a href="https://doi.org/10.21105/joss.05587" class="external-link">doi:10.21105/joss.05587</a>, <a href="https://doi.org/10.21105/joss.05587" class="external-link">https://doi.org/10.21105/joss.05587</a>. 
 </p>
-      <pre>@Manual{,
-  title = {CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},
-  author = {Naeem Khoshnevis and Daniela Maria Garcia and Riccardo Cadei and Kwonsang Lee and Falco Joannes {Bargagli Stoffi}},
+      <pre>@Article{,
+  title = {CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects},
+  author = {Riccardo Cadei and Naeem Khoshnevis and Kwonsang Lee and Daniela Maria Garcia and Falco J. Bargagli Stoffi},
   year = {2023},
-  note = {R package version 0.2.5.9000},
-  url = {https://github.com/NSAPH-Software/CRE},
+  journal = {Journal of Open Source Software},
+  volume = {8},
+  number = {92},
+  pages = {5587},
+  doi = {10.21105/joss.05587},
+  url = {https://doi.org/10.21105/joss.05587},
 }</pre>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 6f39ed39..fb8fed77 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   CRE: CRE.html
   Contribution: Contribution.html
   Testing-the-Package: Testing-the-Package.html
-last_built: 2023-12-15T18:43Z
+last_built: 2023-12-15T19:16Z
 urls:
   reference: https://github.com/NSAPH-Software/CRE/reference
   article: https://github.com/NSAPH-Software/CRE/articles
diff --git a/docs/search.json b/docs/search.json
index 9e3ab17a..ba560e25 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement nkhoshnevis@g.harvard.edu. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"CRE","text":"Installing CRAN. Installing latest developing version. Import.","code":"install.packages(\"CRE\") library(devtools) install_github(\"NSAPH-Software/CRE\", ref = \"develop\") library(\"CRE\")"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"arguments","dir":"Articles","previous_headings":"","what":"Arguments","title":"CRE","text":"Data (required) y observed response/outcome vector (binary continuous). z treatment/exposure/policy vector (binary). X covariate matrix (binary continuous). Parameters (required) method_parameters list parameters define models used, including: - ratio_dis ratio data delegated discovery sub-sample (default: 0.5). - ite_method method estimate individual treatment effect (default: “aipw”) [1]. - learner_ps (SuperLearner) model propensity score estimation (default: “SL.xgboost”, used “aipw”,“bart”,“cf” ITE estimators). - learner_y (SuperLearner) model outcome estimation (default: “SL.xgboost”, used “aipw”,“slearner”,“tlearner” “xlearner” ITE estimators). hyper_params list hyper parameters fine tune method, including: - intervention_vars Intervention-able variables used Rules Generation (default: NULL). - ntrees number decision trees random forest (default: 20). - node_size Minimum size trees’ terminal nodes (default: 20). - max_rules Maximum number candidate decision rules (default: 50). - max_depth Maximum rules length (default: 3). - t_decay decay threshold rules pruning (default: 0.025). - t_ext threshold define generic specific (extreme) rules (default: 0.01). - t_corr threshold define correlated rules (default: 1). - stability_selection Method stability selection selecting rules. vanilla stability selection, error_control stability selection error control stability selection (default: vanilla). - B Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). - subsample Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). - offset Name covariate use offset (.e. “x1”) T-Poisson ITE Estimation. NULL used (default: NULL). - cutoff Threshold defining minimum cutoff value stability scores Stability Selection (default: 0.9). - pfer Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). Additional Estimates (required) ite estimated ITE vector. given, ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"options-for-the-ite-estimation","dir":"Articles","previous_headings":"Arguments > Notes","what":"Options for the ITE estimation","title":"CRE","text":"[1] Options ITE estimation follows: - S-Learner (slearner). - T-Learner (tlearner) - T-Poisson(tpoisson) - X-Learner (xlearner) - Augmented Inverse Probability Weighting (aipw) - Causal Forests (cf) - Causal Bayesian Additive Regression Trees (bart) estimates ITE provided ite additional argument, ITE estimations discovery inference skipped values estimates used instead. ITE estimator requires also outcome learner /propensity score learner SuperLearner package (.e., “SL.lm”, “SL.svm”). models simple classifiers/regressors. default XGBoost algorithm used steps.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"customized-wrapper-for-superlearner","dir":"Articles","previous_headings":"Arguments > Notes","what":"Customized wrapper for SuperLearner","title":"CRE","text":"One can create customized wrapper SuperLearner internal packages. following example providing number cores (e.g., 12) xgboost package shared memory system. use “m_xgboost”, instead “SL.xgboost”.","code":"m_xgboost <- function(nthread = 12, ...) {   SuperLearner::SL.xgboost(nthread = nthread, ...) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"CRE","text":"Example 1 (default parameters) Example 2 (personalized ite estimation) Example 3 (setting parameters) synthetic data sets can generated using generate_cre_dataset().","code":"set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  cre_results <- cre(y, z, X) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\")   y <- dataset[[\"y\"]]   z <- dataset[[\"z\"]]   X <- dataset[[\"X\"]]  ite_pred <- ... # personalized ite estimation cre_results <- cre(y, z, X, ite = ite_pred) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = c(\"x1\",\"x2\",\"x3\",\"x4\"),                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.8,                      pfer = 1,                      B = 10,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"environment-setup","dir":"Articles","previous_headings":"","what":"Environment Setup","title":"Contribution","text":"Please follow steps get copy CRE Github account. Navigate CRE Github repository, top right corner, click Fork button. add clone project Github account. Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): already SSH key, need generate one. Read . Now, can modify codebase track modification. good idea create new branch work codebase. Read following instructions git branching.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"git-branching-model","dir":"Articles","previous_headings":"","what":"Git Branching Model","title":"Contribution","text":"Although, personal repository, can pick branch name, however, order keep consistency also understand working , following convention strongly recommended. project, follow convention proposed Vincent Driessen successful Git branching model post.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"where-to-submit-pull-requests","dir":"Articles","previous_headings":"","what":"Where to submit pull requests?","title":"Contribution","text":"pull requests submitted base repository: fasrc/CRE base: develop branch.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"pull-request-checklist","dir":"Articles","previous_headings":"","what":"Pull request checklist","title":"Contribution","text":"Please run devtools::document(), devtools::load_all() final modifications. Make sure modified code passes checks tests (can run devtools::check() RStudio) PR pass CI reviews can merge . Add line(s) modification NEWS.md file. adding new features, please make sure appropriate documentation added updated. Please clean white spaces. Read .","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"reporting-bugs","dir":"Articles","previous_headings":"","what":"Reporting bugs","title":"Contribution","text":"Please report potential bugs creating new issue sending us email. Please include following information bug report: brief description , expected happen, happened. OS using whether using personal computer HPC cluster. version package installed.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"style-guide","dir":"Articles","previous_headings":"","what":"Style Guide","title":"Contribution","text":"project, follow tidyverse style guide.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"names","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Names","title":"Contribution","text":"File names snake_case ends .R (e.g., create_matching.R) variable names small letter separate _ need (e.g., delta_n) Function names follow snake_case style (e.g., generate_data) Function names follow verb+output convention (e.g., compute_resid)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"spaces-and-indentation","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Spaces and Indentation","title":"Contribution","text":"Indentations two spaces (use tab) Place space around binary operators (e.g., x + y) Place space comma Place space # commenting avoid multiple ### put space opening closing parenthesis Place space () used , , .","code":"#Acceptable: z <- x + y #Not recommended: z<-x+y # (no space) z<- x+y z<-x +y #Acceptable: a <- matrix(c(1:100), nrow = 5) #Not recommended: a <- matrix(c(1:100),nrow = 5) # (no space after comma) a <- matrix( c(1:100), nrow = 5 ) # (extra space after and before parentheses) a<-matrix(c(1:100), nrow = 5) # (no space around unary operator <- ) #Acceptable: # This is a comment #Not recommended: #This is a comment #    This is a comment (more than one space after #) ## This is a comment (multiple #) ###    This is a comment (multiple # and more than one space) #Acceptable: x <- (z + y) #Not recommended: x <- ( z + y ) # (unnecessary space) x <- (z + y ) x <- ( z + y) #Acceptible if (x > 2) {   print(x) } # Not recommended if(x > 2){   print(x) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"other-notes","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Other notes","title":"Contribution","text":"Maximum line length 80 character Use explicit returns Use explicit tags documentation (e.g., @title, @description, …)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"getting-the-code","dir":"Articles","previous_headings":"","what":"Getting the code","title":"Testing the CRE Package","text":"test package, please install package system (R (>= 3.5.0)). can install package following one approaches: Directly GitHub CRAN (recommended) Source Forked Repository (recommended)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-directly-from-github","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package directly from Github","title":"Testing the CRE Package","text":"project, follow successful Git Branching Model. result, develop branch updated branch developers. Use devtools::install_github install package. specify ref, install master (main) branch. Try ?CRE. open package description page help tab (assuming using RStudio).","code":"library(devtools) try(detach(\"package:CRE\", unload = TRUE), silent = TRUE) # if already you have the package, detach and unload it, to have a new install.  install_github(\"NSAPH-Software/CRE\", ref=\"develop\") library(CRE)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-cran","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from CRAN","title":"Testing the CRE Package","text":"Installing package CRAN developing purposes recommended. probably, version CRAN latest version. [Complete section submitting package CRAN]","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-the-source","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from the source","title":"Testing the CRE Package","text":"order install package source, need download source code computer install source. steps: Go package Github repository drop-menu change branch develop. click Code tab click Download Zip tab. Open one files using RStudio, change project directory project directory (Session > Set Working Directory > Project Directory). Load devtools library load CRE.","code":"library(devtools) load_all() ?CRE"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"forking-the-package","dir":"Articles","previous_headings":"Getting the code","what":"Forking the package","title":"Testing the CRE Package","text":"Forking package Github account best option planning installing, testing, modifying, contributing project. Go package Github repository click Fork button top right corner. forking package, Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): Now, can modify codebase track modification. Navigate package folder Install package following Installing package source steps. good idea create new branch work codebase. Read successful Git Branching Model branching convention.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"testing-the-package","dir":"Articles","previous_headings":"","what":"Testing the Package","title":"Testing the CRE Package","text":"Run following command test package.","code":"library(CRE)  # Generate sample data set.seed(1358) dataset <- generate_cre_dataset(n = 1000,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method = \"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\",                       offset = NULL)  hyper_params <- list(intervention_vars = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.01,                      t_corr = 1,                      t_pvalue = 0.05,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 10,                      subsample = 0.5)  # linreg CATE estimation with aipw ITE estimation cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            FASRC Daniela Maria Garcia. Author. Riccardo Cadei. Author. Kwonsang Lee. Author. Falco Joannes Bargagli Stoffi. Author.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Khoshnevis N, Garcia D, Cadei R, Lee K, Bargagli Stoffi F (2023). CRE: Interpretable Discovery Inference Heterogeneous Treatment Effects. R package version 0.2.5.9000, https://github.com/NSAPH-Software/CRE.","code":"@Manual{,   title = {CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects},   author = {Naeem Khoshnevis and Daniela Maria Garcia and Riccardo Cadei and Kwonsang Lee and Falco Joannes {Bargagli Stoffi}},   year = {2023},   note = {R package version 0.2.5.9000},   url = {https://github.com/NSAPH-Software/CRE}, }"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"Identifying subgroups study population treatment exposure notably larger smaller effect outcome compared population average crucial social health sciences. estimating conditional average treatment effect (CATE) given pre-specified set covariates common approach, allows estimating causal effects subgroups specified priori researchers. package implements recently developed Causal Rule Ensemble (CRE) algorithm, flexible stable method interpretable discovery estimation heterogeneous treatment effects terms decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"package-details","dir":"","previous_headings":"","what":"Package Details","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"cre() function executes entire CRE algorithm utilizing data set input parameters user provides. divides study data discovery inference subsamples, generating filtering decision rules discovery subsample explain heterogeneity treatment effects. cre() uses inference subsample estimate linear decomposition Conditional Average Treatment Effect (CATE) terms just discovered decision rules, normal equations model ITE estimates. steps, cre() agnostic concerning method used ITE estimation. estimate Individual Treatment Effects (ITE), users can choose various methodologies vary terms speed accuracy. Several ITE estimators internally implemented (.e. AIPW, Causal Forest, Bayesian Causal Forest, Causal BART, S-Learner, T-Learner, X-Learner), customized ITE estimation can also provided input. results collected S3 object containing: number decision rules extracted step, CATE decomposition estimates corresponding uncertainty quantification, list selected parameters, predicted ITEs.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"applications","dir":"","previous_headings":"","what":"Applications","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"CRE package can used diverse theoretical practical applications long working binary exposure treatment level. example, can used education determine teaching methods effective specific student subgroups. marketing, can identify effective strategies different consumer subgroups. healthcare, can used identify patient subgroups benefit particular treatment intervention. can also used studies personalized medicine subgroup patients responds differently provided medicine. public health, CRE method applied discover subgroups vulnerable (resilient) causal effects long-term exposure air pollution mortality.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The CRE package — CRE-package","title":"The CRE package — CRE-package","text":"health social sciences, critically important identify subgroups study population treatment notable heterogeneity causal effects respect average treatment effect. Data-driven discovery heterogeneous treatment effects (HTE) via decision tree methods proposed task. Despite high interpretability, single-tree discovery HTE tends highly unstable find oversimplified representation treatment heterogeneity. accommodate shortcomings, propose Causal Rule Ensemble (CRE), new method discover heterogeneous subgroups ensemble--trees approach. CRE following features: provides interpretable representation HTE; 2) allows extensive exploration complex heterogeneity patterns; 3) guarantees high stability discovery. discovered subgroups defined terms interpretable decision rules, develop general two-stage approach subgroup-specific conditional causal effects estimation, providing theoretical guarantees.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The CRE package — CRE-package","text":"Bargagli-Stoffi, F. J., Cadei, R., Lee, K. Dominici, F. (2023). Causal rule ensemble: Interpretable Discovery Inference Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The CRE package — CRE-package","text":"Naeem Khoshnevis Daniela Maria Garcia Riccardo Cadei Kwonsang Lee Falco Joannes Bargagli Stoffi","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Sensitivity Analysis — analyze_sensitivity","title":"Sensitivity Analysis — analyze_sensitivity","text":"Method analyzing sensitivity estimates causal rule-specific treatment effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sensitivity Analysis — analyze_sensitivity","text":"","code":"analyze_sensitivity(ite_std, rules_matrix_std)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sensitivity Analysis — analyze_sensitivity","text":"ite_std standardized ITE rules_matrix_std standardized causal rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sensitivity Analysis — analyze_sensitivity","text":"list containing results sensitivity analysis","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cre object — autoplot.cre","title":"A helper function for cre object — autoplot.cre","text":"helper function plot cre object using ggplot2 package.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cre object — autoplot.cre","text":"","code":"# S3 method for cre autoplot(object, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cre object — autoplot.cre","text":"object cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cre object — autoplot.cre","text":"Returns ggplot object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_hyper_params","title":"Check input parameters — check_hyper_params","text":"Checks consistency input (hyper) parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_hyper_params","text":"","code":"check_hyper_params(X_names, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_hyper_params","text":"X_names observed covariates names. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_hyper_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_input","title":"Check input parameters — check_input","text":"Checks input parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_input","text":"","code":"check_input(y, z, X, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_input","text":"y observed response vector. z treatment vector. X covariate matrix. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_input","text":"list params might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input data — check_input_data","title":"Check input data — check_input_data","text":"Conducts sanity checks input data.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input data — check_input_data","text":"","code":"check_input_data(y, z, X, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input data — check_input_data","text":"y observed response vector. z treatment vector. X features matrix. ite estimated ITE vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input data — check_input_data","text":"number data samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check method-related parameters — check_method_params","title":"Check method-related parameters — check_method_params","text":"Checks method-related parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check method-related parameters — check_method_params","text":"","code":"check_method_params(y, ite, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check method-related parameters — check_method_params","text":"y observed response vector. ite estimated ITE vector. params list parameters required run method functions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check method-related parameters — check_method_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Causal rule ensemble — cre","title":"Causal rule ensemble — cre","text":"Performs Causal Rule Ensemble data set response variable, treatment variable, various features.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Causal rule ensemble — cre","text":"","code":"cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Causal rule ensemble — cre","text":"y observed response vector. z treatment vector. X covariate matrix (data frame). provided numerical values. method_params list parameters define models used, including: Parameters Honest Splitting ratio_dis: ratio data delegated rules discovery (default: 0.5). Parameters Discovery Inference ite_method: method ITE (pseudo-outcome) estimation (default: \"aipw\", options: \"aipw\" Augmented Inverse Probability Weighting, \"cf\" Causal Forest, \"bart\" Causal Bayesian Additive Regression Trees, \"slearner\" S-Learner, \"tlearner\" T-Learner, \"xlearner\" X-Learner, \"tpoisson\" T-Learner Poisson regression). learner_ps: model propensity score estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"bart\", \"cf\" ITE estimators). learner_y: model outcome estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"slearner\", \"tlearner\" \"xlearner\" ITE estimators). hyper_params list hyper parameters fine-tune method, including: General hyper parameters intervention_vars: Array intervention-able covariates names used Rules Generation. Empty null array means covariates considered intervention-able (default: NULL). ntrees: number decision trees random forest (default: 20). node_size: Minimum size trees' terminal nodes (default: 20). max_rules: Maximum number generated candidates rules (default: 50). max_depth: Maximum rules length (default: 3). t_decay: decay threshold rules pruning. Higher values carry aggressive pruning (default: 0.025). t_ext: threshold truncate generic specific (extreme) rules (default: 0.01, range: [0, 0.5)). t_corr: threshold define correlated rules (default: 1, range: [0,+inf)). stability_selection: Method stability selection selecting rules. \"vanilla\" stability selection, \"error_control\" stability selection error control  \"\" stability selection (default: \"vanilla\"). B: Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). subsample: Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). Method specific hyper parameters offset: Name covariate use offset (.e., \"x1\") T-Poisson ITE estimation. Use NULL offset used (default: NULL). cutoff:  Threshold (percentage) defining minimum cutoff value stability scores Stability Selection (default: 0.9). pfer: Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). ite estimated ITE vector. given ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Causal rule ensemble — cre","text":"S3 object composed : M number Decision Rules extracted step, CATE data.frame Conditional Average Treatment Effect decomposition estimates corresponding uncertainty quantification, method_params list method parameters, hyper_params list hyper parameters, rules list rules (implicit form) decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Causal rule ensemble — cre","text":"intervention_vars provided, important note individual treatment effect still computed using covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Causal rule ensemble — cre","text":"","code":"# \\donttest{ set.seed(123) dataset <- generate_cre_dataset(n = 400,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = NULL,                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 20,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) #> Loading required package: nnls #> Registered S3 method overwritten by 'randomForest': #>   method      from #>   plot.margin RRF  # }"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":null,"dir":"Reference","previous_headings":"","what":"The Causal Rule Ensemble Crossfit — cre_crossfit","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"Performs Causal Rule Ensemble twice dataset response variable, treatment variable, various features, swapping discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"cre_crossfit(   y,   z,   X,   ite_method_dis,   include_ps_dis = NA,   ps_method_dis = \"SL.xgboost\",   or_method_dis = NA,   ite_method_inf,   include_ps_inf = NA,   ps_method_inf = \"SL.xgboost\",   or_method_inf = NA,   ntrees_rf,   ntrees_gbm,   min_nodes,   max_nodes,   t,   q,   rules_method = NA,   include_offset = FALSE,   offset_name = NA,   cate_method = \"DRLearner\",   cate_SL_library = \"SL.xgboost\",   filter_cate = FALSE )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"y observed response vector z treatment vector X covariate matrix ite_method_dis method estimate discovery sample ITE include_ps_dis whether include propensity score estimate covariate discovery ITE estimation, considered BART, XBART, CF ps_method_dis estimation model propensity score discovery subsample or_method_dis estimation model outcome regressions estimate_ite_aipw discovery subsample ite_method_inf method estimate inference sample ITE include_ps_inf whether include propensity score estimate covariate inference ITE estimation, considered BART, XBART, CF ps_method_inf estimation model propensity score inference subsample or_method_inf estimation model outcome regressions estimate_ite_aipw inference subsample ntrees_rf number decision trees randomForest ntrees_gbm number decision trees gradient boosting min_nodes minimum size trees' terminal nodes max_nodes maximum size trees' terminal nodes t common support used generating causal rules matrix q selection threshold used selecting causal rules rules_method method selecting causal rules binary outcomes include_offset whether include offset estimating ITE, poisson offset_name name offset, included cate_method method estimate CATE values cate_SL_library library used cate_method set DRLearner filter_cate whether filter rules p-value <= 0.05","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"S3 object containing matrix averaged Conditional Average Treatment Effect estimates two CRE iterations","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  cre_cross_results <- cre_crossfit(y = dataset[[\"y\"]], z = dataset[[\"z\"]],                                   X = as.data.frame(dataset[[\"X\"]]),                                   ite_method_dis = \"bart\",                                   include_ps_dis = TRUE,                                   ite_method_inf = \"bart\",                                   include_ps_inf = TRUE,                                   ntrees_rf = 100, ntrees_gbm = 50,                                   min_nodes = 20, max_nodes = 5,                                   t = 0.025, q = 0.8) #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> Conducting Discovery Subsample Analysis 1 #> 411 rules (length<=15) were extracted from the first 100 trees. #> 150 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 1 #> Conducting Discovery Subsample Analysis 2 #> 493 rules (length<=15) were extracted from the first 100 trees. #> 100 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 2"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"Discard rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"","code":"discard_anomalous_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"Discard redundant correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"","code":"discard_correlated_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover causal decision rules — discover_causal_rules","title":"Discover causal decision rules — discover_causal_rules","text":"Discovers Causal Decision Rules randomization-based tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover causal decision rules — discover_causal_rules","text":"","code":"discover_causal_rules(   rules_matrix,   rules_list,   ite,   stability_selection,   cutoff,   pfer,   penalty_rl )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover causal decision rules — discover_causal_rules","text":"rules_matrix causal rules matrix. rules_list vector causal rules. ite estimated ITE. stability_selection Whether using stability selection select causal rules. cutoff Threshold (percentage) defining minimum cutoff value stability scores. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). penalty_rl Order penalty rules length LASSO Causal Rules Discovery (.e. 0: penalty, 1: rules_length, 2: rules_length^2)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover causal decision rules — discover_causal_rules","text":"List Causal Decision Rules discovered","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover rules — discover_rules","title":"Discover rules — discover_rules","text":"Discover minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover rules — discover_rules","text":"","code":"discover_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover rules — discover_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover rules — discover_rules","text":"minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Conditional Average Treatment Effect — estimate_cate","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"Estimates Conditional Average Treatment Effect (CATE) linearly modeling Individual Treatment Effect set rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"","code":"estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"rules_matrix rules matrix. rules_explicit list select rules terms covariate names. ite estimated ITEs. B number bootstrap samples uncertainty quantification estimation. subsample bootstrap ratio subsample uncertainty quantification estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"list 2 elements: summary: data frame summarizing CATE linear decomposition: Rule: rule name, Estimate: linear contribution CATE, CI_lower: lower bound 95% confidence interval estimate, CI_upper: upper bound 95% confidence interval estimate, P_Value: p-value (Z-test). model: linear model CATE-ATE estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"Estimates Individual Treatment Effect given response vector, treatment vector, covariate matrix, desired algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"","code":"estimate_ite(y, z, X, ite_method, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"y observed response vector. z observed treatment vector. X covariate matrix. ite_method method estimating Individual Treatment Effect. methods requires additional parameters. parameters mentioned indented blocks method definitions provided end parameters list. \"slearner\": S-Learner. - learner_y \"tlearner\": T-Learner. learner_y \"tpoisson\": T-Poisson. offset \"xlearner\": X-Learner. learner_y \"aipw\": Augmented Inverse Probability Weighting. learner_ps  learner_y \"bart\": Bayesian Additive Regression Trees. learner_ps \"cf\": Causal Forest. learner_ps ... Additional parameters passed different models.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"list ITE estimates.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"additional-parameters","dir":"Reference","previous_headings":"","what":"Additional parameters","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"learner_ps: estimation method propensity score. includes libraries SuperLearner package. learner_y: estimation model outcome. includes libraries SuperLearner package. offset: Name covariate use offset (.e. \"x1\") Poisson ITE Estimation. NULL offset used.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"Estimates Individual Treatment Effect using Augmented Inverse Probability Weighting given response vector, treatment vector, features matrix, estimation model propensity score estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"","code":"estimate_ite_aipw(y, z, X, learner_ps = \"SL.xgboost\", learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"y observed response vector. z treatment vector. X features matrix. learner_ps estimation model propensity score. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"Estimates Individual Treatment Effect using Bayesian Additive Regression Trees given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"","code":"estimate_ite_bart(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"y observed response vector. z treatment vector. X features matrix. learner_ps Method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"number samples number burn set default equal 500.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"Estimates Individual Treatment Effect using Bayesian Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"","code":"estimate_ite_bcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"y observed response vector. z treatment vector. X features matrix. ps_method method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"Estimates Individual Treatment Effect using Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"","code":"estimate_ite_cf(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"y observed response vector. z treatment vector. X features matrix. learner_ps method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"Estimates Individual Treatment Effect (ITE) using Inverse Propensity Weighting (IPW) given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"","code":"estimate_ite_ipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"Method estimating Individual Treatment Effect using Outcome Regression given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"estimate_ite_or(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"y observed response vector z treatment vector X features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  # Initialize parameters y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- as.data.frame(dataset[[\"X\"]])  ite_list <- estimate_ite_or(y, z, X) #> *****Into main of wbart #> *****Data: #> data:n,p,np: 388, 10, 612 #> y1,yn: -0.694294, 1.362455 #> x1,x[n*p]: 1.000000, 1.000000 #> xp1,xp[np*p]: 0.000000, 0.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.118817,3.000000,0.250422 #> *****sigma: 1.133838 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000 #> *****Into main of wbart #> *****Data: #> data:n,p,np: 612, 10, 388 #> y1,yn: -0.485955, 0.857738 #> x1,x[n*p]: 0.000000, 0.000000 #> xp1,xp[np*p]: 1.000000, 1.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.139249,3.000000,0.168737 #> *****sigma: 0.930723 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"Estimates Individual Treatment Effect (ITE) using Outcome Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"","code":"estimate_ite_oreg(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"y observed response vector. z treatment vector. X features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"Estimates Individual Treatment Effect using Poisson Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"","code":"estimate_ite_poisson(y, z, X, X_names, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"y observed response vector. z treatment vector. X features matrix. X_names vector includes names covariates. offset name covariate use offset (.e. 'x1') model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"Estimates Individual Treatment Effect using Stabilized Inverse Propensity Weighting given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"","code":"estimate_ite_sipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"Estimates Individual Treatment Effect using S-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"","code":"estimate_ite_slearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"Estimates Individual Treatment Effect using T-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"","code":"estimate_ite_tlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"Estimates Individual Treatment Effect using Poisson regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"","code":"estimate_ite_tpoisson(y, z, X, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"y observed response vector. z treatment vector. X features matrix. offset name covariate use offset (.e. “x1”) model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"","code":"estimate_ite_xbart(y, z, X, include_ps, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"y observed response vector z treatment vector X features matrix include_ps whether include propensity score estimate covariate ITE estimation ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Causal Forest given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"","code":"estimate_ite_xbcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"y observed response vector z treatment vector X features matrix ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"Estimates Individual Treatment Effect using X-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"","code":"estimate_ite_xlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the propensity score — estimate_ps","title":"Estimate the propensity score — estimate_ps","text":"Estimates Propensity Score given treatment vector features data frame.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the propensity score — estimate_ps","text":"","code":"estimate_ps(z, X, ps_method = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the propensity score — estimate_ps","text":"z treatment vector. X features data frame. ps_method estimation model propensity score (default: SL.xgboost).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the propensity score — estimate_ps","text":"vector propensity score estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":null,"dir":"Reference","previous_headings":"","what":"Discovery (performance) evaluation — evaluate","title":"Discovery (performance) evaluation — evaluate","text":"Computes different metrics discovery evaluation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discovery (performance) evaluation — evaluate","text":"","code":"evaluate(ground_truth, prediction)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discovery (performance) evaluation — evaluate","text":"ground_truth List true (CDR/EM) values. prediction List predicted (CDR/EM) values.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discovery (performance) evaluation — evaluate","text":"Intersection union, precision, recall.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract effect modifiers — extract_effect_modifiers","title":"Extract effect modifiers — extract_effect_modifiers","text":"Extracts effect modifiers list (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract effect modifiers — extract_effect_modifiers","text":"","code":"extract_effect_modifiers(rules_list, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract effect modifiers — extract_effect_modifiers","text":"rules_list list (causal) decision rules. X_names list covariate names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract effect modifiers — extract_effect_modifiers","text":"list effect modifiers.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract (causal) decision rules — extract_rules","title":"Extract (causal) decision rules — extract_rules","text":"Extracts causal rules random forest gradient boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract (causal) decision rules — extract_rules","text":"","code":"extract_rules(treelist, X, max_depth, digits = 2)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract (causal) decision rules — extract_rules","text":"treelist list decision trees. X Features matrix. max_depth number top levels tree considered. digits Number digits rounding decision rules extract conditions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract (causal) decision rules — extract_rules","text":"vector (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter correlated rules — filter_correlated_rules","title":"Filter correlated rules — filter_correlated_rules","text":"Discards highly correlated rules (.e., \\(Cov(rule_1,rule_2) > t_{corr}\\)).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter correlated rules — filter_correlated_rules","text":"","code":"filter_correlated_rules(rules_matrix, rules_list, t_corr)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter correlated rules — filter_correlated_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_corr threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter correlated rules — filter_correlated_rules","text":"rules matrix without highly correlated rules (columns).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter extreme decision rules — filter_extreme_rules","title":"Filter extreme decision rules — filter_extreme_rules","text":"Discards rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter extreme decision rules — filter_extreme_rules","text":"","code":"filter_extreme_rules(rules_matrix, rules_list, t_ext)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_ext threshold [0, 0.5) range truncate generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules matrix without rare/common rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"Filters irrelevant decision rules. irrelevant rules interpreted error increase removing variable-value pair decision rules (see “Interpreting tree ensembles inTrees package” Houtao Deng, 2019).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"","code":"filter_irrelevant_rules(rules, X, ite, t_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"rules list rules. X features matrix. ite estimated ITE. t_decay decay threshold rules pruning.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"list “relevant” rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate causal rules — generate_causal_rules","title":"Generate causal rules — generate_causal_rules","text":"Generates causal decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate causal rules — generate_causal_rules","text":"","code":"generate_causal_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate causal rules — generate_causal_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate causal rules — generate_causal_rules","text":"vector causal rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate CRE synthetic data — generate_cre_dataset","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Generates synthetic data sets run simulation causal inference experiments composed outcome vector (y), treatment vector (z), covariates matrix (X), unobserved individual treatment effects vector (ite). arguments specify data set characteristic, including number individuals (n), number covariates (p), correlation within covariates (rho),  number decision rules (n_rules) decomposing Conditional Average Treatment Effect (CATE), treatment effect magnitude (effect_size), confounding mechanism (confounding), whether covariates outcomes binary continuous (binary_covariates, binary_outcome).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"generate_cre_dataset(   n = 1000,   rho = 0,   n_rules = 2,   p = 10,   effect_size = 2,   binary_covariates = TRUE,   binary_outcome = TRUE,   confounding = \"no\" )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate CRE synthetic data — generate_cre_dataset","text":"n integer number represents number observations. Non-integer values converted integer number. rho positive double number represents correlation within covariates (default: 0, range: [0,1)). n_rules number causal rules (default: 2, range: {1,2,3,4}). p number covariates (default: 10). effect_size treatment effect size magnitude (default: 2, range: \\(\\geq\\)0). binary_covariates Whether use binary continuous covariates (default: TRUE). binary_outcome Whether use binary continuous outcomes (default: TRUE). confounding continuous outcome, add confounding variables: \"lin\" linear confounding, \"nonlin\" non-linear confounding, \"\" confounding (default).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate CRE synthetic data — generate_cre_dataset","text":"list, representing generated synthetic data set, containing: y outcome vector, z treatment vector, X covariates matrix, ite individual treatment vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate CRE synthetic data — generate_cre_dataset","text":"covariates matrix generated specified correlation among individuals, covariate sampled either Bernoulli(0.5) binary, Gaussian(0,1) continuous. treatment vector sampled Bernoulli(\\(\\frac{1}{1+ \\exp(1-x_1+x_2-x_3)}\\)), enforcing treatment assignment probabilities function observed covariates. potential outcomes (\\(y(0)\\) \\(y(1)\\)) sampled Bernoulli binary, Gaussian (standard deviation equal 1) continuous. mean equal confounding term (null, linear non-linear always null binary outcome) plus 1-4 decision rules weighted treatment effect magnitude. two potential outcomes characterizes CATE (unobserved individual treatment effects vector) sum different additive contributions decision rules considered (plus intercept). final expression CATE depends treatment effect magnitude number decision rules considered. 4 decision rules : Rule 1: \\(1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x})\\) Rule 2: \\(1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})\\) Rule 3: \\(1\\{x_4 \\leq 0.5\\}(\\textbf{x})\\) Rule 4: \\(1\\{x_5 \\leq 0.5; x_7 > 0.5; x_8 \\leq 0.5\\}(\\textbf{x})\\) corresponding additive average treatment effect (AATE) equal : Rule 1: \\(-\\) effect_size, Rule 2: \\(+\\) effect_size, Rule 3: \\(- 0.5 \\cdot\\) effect_size, Rule 4: \\(+ 2 \\cdot\\) effect_size. example, setting effect_size=4 n_rules=2: $$\\text{CATE}(\\textbf{x}) = -4 \\cdot 1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x}) + 4 \\cdot 1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})$$ final outcome vector y finally computed combining potential outcomes according treatment assignment.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Set covariates domain (binary_covariates) outcome domain (binary_outcome) according experiment interest. Increase complexity heterogeneity discovery: decreasing sample size (n), adding correlation among covariates (rho), increasing number rules (n_rules), increasing number covariates (p), decreasing absolute value causal effect (effect_size), adding linear -linear confounders (confounding).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"set.seed(123) dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary_covariates = TRUE,                                 binary_outcome = TRUE, confounding = \"no\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules — generate_rules","title":"Generate rules — generate_rules","text":"Generates list rules characterizing heterogeneity Conditional Average Treatment Effect (CATE) tree-based methods (.e., random forest).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules — generate_rules","text":"","code":"generate_rules(X, ite, ntrees, node_size, max_rules, max_depth)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules — generate_rules","text":"X covariate matrix. ite vector estimated ITE. ntrees number decision trees random forest algorithm. node_size Minimum size trees' terminal nodes. max_rules Maximum number candidate decision rules. max_depth Maximum rules length.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules — generate_rules","text":"list rules (names).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules matrix — generate_rules_matrix","title":"Generate rules matrix — generate_rules_matrix","text":"Generates rules matrix feature covariate matrix vector rules. number rows rules_matrix equal number samples X, number columns equal number rules rules_list. element rules_matrix corresponds specific data sample rule. data sample satisfies rule, corresponding element rules_matrix set 1. Otherwise, element set 0.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules matrix — generate_rules_matrix","text":"","code":"generate_rules_matrix(X, rules_list)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules matrix — generate_rules_matrix","text":"X Features matrix. rules_list vector rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules matrix — generate_rules_matrix","text":"causal rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Logger settings — get_logger","title":"Get Logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":null,"dir":"Reference","previous_headings":"","what":"Honest splitting — honest_splitting","title":"Honest splitting — honest_splitting","text":"Splits data discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Honest splitting — honest_splitting","text":"","code":"honest_splitting(y, z, X, ratio_dis, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Honest splitting — honest_splitting","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis double number indicating ratio data delegated discovery sub-sample. ite vector estimated ITE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Honest splitting — honest_splitting","text":"list containing discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret rules — interpret_rules","title":"Interpret rules — interpret_rules","text":"Replaces column numbers rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret rules — interpret_rules","text":"","code":"interpret_rules(rules, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret rules — interpret_rules","text":"rules vector rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret rules — interpret_rules","text":"list explicit (human-readable) rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret select rules — interpret_select_rules","title":"Interpret select rules — interpret_select_rules","text":"Replaces column numbers select rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret select rules — interpret_select_rules","text":"","code":"interpret_select_rules(select_rules_dis, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret select rules — interpret_select_rules","text":"select_rules_dis vector select causal rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret select rules — interpret_select_rules","text":"vector select causal rules interpretable.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"","code":"lasso_rules_filter(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q Number (unique) selected rules per subsample stability selection. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cre class — plot.cre","title":"Extend generic plot functions for cre class — plot.cre","text":"wrapper function extend generic plot functions cre class.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cre class — plot.cre","text":"","code":"# S3 method for cre plot(x, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cre class — plot.cre","text":"x cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cre class — plot.cre","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict individual treatment effect via causal rule ensemble — predict.cre","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"Predicts individual treatment effect via causal rule ensemble algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"","code":"# S3 method for cre predict(object, X, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"object cre object running CRE function. X covariate matrix (data.frame) ... Additional arguments passed customize prediction.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"array estimated Individual Treatment Effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for the CRE object — print.cre","title":"Extend print function for the CRE object — print.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for the CRE object — print.cre","text":"","code":"# S3 method for cre print(x, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for the CRE object — print.cre","text":"x cre object running CRE function. verbose Set level results description details: 0 results summary, 1 results parameters summary, 2 results parameters rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for the CRE object — print.cre","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune (Causal) Decision Rules — prune_rules","title":"Prune (Causal) Decision Rules — prune_rules","text":"Method pruning (Causal) Decision Rules extracted Random Forest Gradient Boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune (Causal) Decision Rules — prune_rules","text":"","code":"prune_rules(rules, X, ite_std, max_decay, type_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune (Causal) Decision Rules — prune_rules","text":"rules list decision rules. X features matrix. ite_std standardized ITE. max_decay Decay Threshold pruning rules. type_decay Decay Type pruning rules (1: relative error; 2: error).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune (Causal) Decision Rules — prune_rules","text":"list (Causal) Decision Rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select Causal Rules — select_causal_rules","title":"Select Causal Rules — select_causal_rules","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select Causal Rules — select_causal_rules","text":"","code":"select_causal_rules(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select Causal Rules — select_causal_rules","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q selection threshold used selecting causal rules. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select Causal Rules — select_causal_rules","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select rules — select_rules","title":"Select rules — select_rules","text":"Given set rules, selects minimal set linearly decomposing Conditional Average Treatment Effect (CATE) LASSO (optionally Stability Selection).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select rules — select_rules","text":"","code":"select_rules(rules_matrix, rules, ite, stability_selection, cutoff, pfer, B)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select rules — select_rules","text":"rules_matrix rules matrix. rules list rules. ite estimated ITE. stability_selection Stability selection method. cutoff Threshold (percentage) defining minimum cutoff value stability scores. stability selection. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). stability selection. B Number bootstrap samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select rules — select_rules","text":"minimal set rules linearly decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Logger settings — set_logger","title":"Set Logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"CRE.log\", logger_level = \"INFO\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: CRE.log) logger_level log level. log level set, log levels also activated (implemented). Available levels include: TRACE: Provides verbose detailed logging, including steps taken achieve result, often used debugging. Activating TRACE also enable DEBUG, INFO, SUCCESS, WARN, ERROR, FATAL logs. DEBUG: Provides detailed logging flow application, used mostly developers understand potential issues. Activating DEBUG also enable INFO, SUCCESS, WARN, ERROR, FATAL logs. INFO (Default): Standard messages inform user normal operation system. Activating INFO also enable SUCCESS, WARN, ERROR, FATAL logs. SUCCESS: Messages indicating successful completion particular operation task. Activating SUCCESS also enable WARN, ERROR, FATAL logs. WARN: Warning messages events might cause problems future, yet errors. Activating WARN also enable ERROR FATAL logs. ERROR: Reports error due system may able achieve functionality, application halt. Activating ERROR also enable FATAL logs. FATAL: Reports severe error events presumably lead application abort.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set Logger settings — set_logger","text":"Log levels specified developers initial implementation. Future developers contributors can leverage log levels better capture document application's processes events.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Logger settings — set_logger","text":"","code":"set_logger(\"Debug\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Data — split_data","title":"Split Data — split_data","text":"Splits data discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Data — split_data","text":"","code":"split_data(y, z, X, ratio_dis)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Data — split_data","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis ratio data delegated discovery subsample","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Data — split_data","text":"list containing discovery inference subsamples","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardize Rules Matrix — standardize_rules_matrix","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardize (.e. mean=0 stdev=1) rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"","code":"standardize_rules_matrix(rules_matrix)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"rules_matrix rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardized rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of CRE object — summary.cre","title":"Print summary of CRE object — summary.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of CRE object — summary.cre","text":"","code":"# S3 method for cre summary(object, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of CRE object — summary.cre","text":"object cre object running CRE function. verbose Set level results description details: results summary 0, results+parameters summary 1, results+parameters+rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of CRE object — summary.cre","text":"summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":null,"dir":"Reference","previous_headings":"","what":"Take 1 — take1","title":"Take 1 — take1","text":"Generate list indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take 1 — take1","text":"","code":"take1(len)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take 1 — take1","text":"len length value","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take 1 — take1","text":"vector indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take 1 — take1","text":"","code":"ind <- CRE:::take1(10)"},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-025-2023-12-6","dir":"Changelog","previous_headings":"","what":"CRE 0.2.5 (2023-12-6)","title":"CRE 0.2.5 (2023-12-6)","text":"CRAN release: 2023-12-06","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-5","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.5 (2023-12-6)","text":"Add (vanilla) Stability Selection (without Error Control). max_rules hyper parameters max rules filtering. Uncertainty Quantification estimation bootstrapping. B hyper-parameter, subsample hyper-parameter. rules(implicit form) cre() function return. predict() function ITE estimation via CRE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-5","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.5 (2023-12-6)","text":"Type stability_selection binary -> string (‘’,‘vanilla’,‘error_control’). Unify ntrees_gbm hyper-parameter ntrees_gbm hyper-parameter ntrees hyper-parameter. rules generation retrieve decision rules also internal nodes, just terminal nodes. ite_method_dis, ite_method_inf method-parameter -> ite_method. ps_method_dis, ps_method_inf method-parameter -> learner_ps. oreg_method_dis, oreg_method_inf method-parameter -> learner_y.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-5","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.5 (2023-12-6)","text":"max_nodes hyper-parameter. Remove rules generation Generalized Boosted Regression. replace hyper-parameter. penalty_rl hyper-parameter. t_pvalue hyper-parameter. ite_pred cre() function return.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.5 (2023-12-6)","text":"Error saving covariates name CRE result using intervention_vars.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-024-2023-6-14","dir":"Changelog","previous_headings":"","what":"CRE 0.2.4 (2023-6-14)","title":"CRE 0.2.4 (2023-6-14)","text":"CRAN release: 2023-06-14","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-4","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.4 (2023-6-14)","text":"Method paper description updated.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-023-2023-4-27","dir":"Changelog","previous_headings":"","what":"CRE 0.2.3 (2023-4-27)","title":"CRE 0.2.3 (2023-4-27)","text":"CRAN release: 2023-04-27","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-3","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.3 (2023-4-27)","text":"Bayesian Causal Forest (bcf) ITE estimator supported.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-022-2023-4-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.2 (2023-4-17)","title":"CRE 0.2.2 (2023-4-17)","text":"CRAN release: 2023-04-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-2","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.2 (2023-4-17)","text":"Fixed failing unit tests specific operating systems.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-021-2023-3-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.1 (2023-3-17)","title":"CRE 0.2.1 (2023-3-17)","text":"CRAN release: 2023-03-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.1 (2023-3-17)","text":"Replace BATE ATE CATE Linear Decomposition. Update plot() function (remove ATE, old BATE, explicit AATEs).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.1 (2023-3-17)","text":"Code Conduct.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.1 (2023-3-17)","text":"Causal Tree benchmark functional tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.1 (2023-3-17)","text":"Rank-Deficient Rule Matrix Issue (redundant rules). Intervention Variables Filtering (ordered filtering).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-020-2023-1-19","dir":"Changelog","previous_headings":"","what":"CRE 0.2.0 (2023-1-19)","title":"CRE 0.2.0 (2023-1-19)","text":"CRAN release: 2023-01-19","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.0 (2023-1-19)","text":"offset method-parameter -> hyper-parameter estimate_ite_poisson function -> estimate_ite_tpoisson max_dacay hyper-parameter -> t_decay. interpret_select_rules function -> interpret_rules. generate_causal_rules function -> discover_rules. discover_causal_rules function ->select_rules. offset_name method parameter -> offset. Hyper method parameters required arguments cre. cre object: added parameters ite estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.0 (2023-1-19)","text":"Synthetic data set 1 3 rules (generate_cre_dataset). S-Learner (slearner) method ITE estimation. T-Learner (tlearner) method ITE estimation. X-Learner (xlearner) method ITE estimation. Rules Selection description summary.cre. verbose parameter summary.cre. ite, additional cre input parameter use personalized ite estimations. Default values hyper parameters. Default values method parameters. Simulation experiments estimation (estimation.R). Simulation experiments discovery (discovery.R). extract_effect_modifiers function (utility performance evaluation). evaluate function discovery evaluation. confounding parameter generate_cre_dataset set confounding type. ite_pred model CRE results. binary_covariates parameter generate_cre_dataset set covariates domain.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.0 (2023-1-19)","text":"include_ps_inf method-parameter. include_ps_dis method-parameter. oreg method ITE estimation. ipw method ITE estimation. sipw method ITE estimation. ITE standard deviation estimation. type_decay hyper-parameter. Keep linreg CATE estimation (remove cate_method cate_SL_library parameters). method_params hyper_params additional parameters summary.cre. ite standardization Rules Generation. random_state parameter. include_offset method parameter.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.0 (2023-1-19)","text":"Rules Generation Issue (set rules length fix bootstrapping).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-011-2022-10-18","dir":"Changelog","previous_headings":"","what":"CRE 0.1.1 (2022-10-18)","title":"CRE 0.1.1 (2022-10-18)","text":"CRAN release: 2022-10-22","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.1 (2022-10-18)","text":"binary parameter generate_cre_dataset -> binary_outcome . filter_cate hyper-parameter -> t_pvalue. t_anom hyper-parameter -> t_ext. effect_modifier hyper-parameter -> intervention_vars. lasso_rules_filter function -> discover_causal_rules. split_data function -> honest_splitting. prune_rules function -> `filter_irrelevant_rules. discard_correlated_rules function -> filter_correlated_rules. discard_anomalous_rules function -> filter_extreme_rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.1 (2022-10-18)","text":"Weighted LASSO Causal Rules Discovery (penalty_rl hyper-parameter).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-010-2022-10-17","dir":"Changelog","previous_headings":"","what":"CRE 0.1.0 (2022-10-17)","title":"CRE 0.1.0 (2022-10-17)","text":"CRAN release: 2022-10-18","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.0 (2022-10-17)","text":"Update examples tests functions. q hyper-parameter -> cutoff. pfer_val hyper-parameter -> pfer. select_causal_rules function -> lasso_rules_filter. t hyper-parameter -> t_anom. Separate standardization, remove filtering generate_rules_matrix function. summary.cre function describe results. min_nodes hyper-parameter -> node_size (randomForest convention). cre returns S3 object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.0 (2022-10-17)","text":"Examples tests functions. prune_rules function discard un-predictive rules. discard_anomalous_rules function discard anomalous rules (see t_corr hyper-parameter.). discard_correlated_rules function discard correlated rules (see t_anom hyper-parameter). effect_modifiers parameter generate_rules function covariates filtering. generate_causal_rules function. Helper function SuperLearner package propensity score estimation estimate_ite_xyz. Five methods CATE estimation (poisson, DRLearner, bart-baggr, cf-means, linreg) estimate_cate function. (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) method-parameters complement SuperLearner package. cate_method method-parameter select CATE estimation method. filter_cate method-parameter estimation filtering. p parameter (generate_cre_dataset function) set number covariates. replace parameter (generate_rules function) allow bootstrapping. cre.print generic function print cre S3 object results. cre.summary generic functions summarize cre S3 object Results. check_input function isolate input checks. estimate_ite_aipw function augmented inverse propensity weighting. plot.cre generic function plot cre S3 object results. test-cre_functional.R test functionality package. stability_selection function causal rules selection.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-1-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.1.0 (2022-10-17)","text":"estimate_ite_blp function. take1() function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.1.0 (2022-10-17)","text":"Undesired ‘’ Decision Rule Issue. Causal Rule Selected Issue.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.0.1 (2021-10-20)","text":"estimate_cate include two methods estimating CATE values. cre added initial checks binary outcome whether include propensity score ITE estimation. estimate_ite_xyz conduct propensity score estimation using helper function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.0.1 (2021-10-20)","text":"Example generate_cre_dataset. set_logger get_logger. check_input_data function. generate_cre_dataset function generate synthetic data testing package. test-generate_cre_dataset function test. estimate_ps function estimate propensity score. estimate_ite_xbart function generate ITE estimates using accelerated BART. estimate_ite_xbcf function generate ITE estimates using accelerated BCF. analyze_sensitivity function conduct sensitivity analysis unmeasured confounding. cre function perform entire Causal Rule Ensemble method. estimate_cate function generate CATE estimates ITE estimates select rules. estimate_ite function generate ITE estimates using user-specified method (calls estimate_ite_xyz functions). estimate_ite_bart function generate ITE estimates using BART. estimate_ite_bcf function generate ITE estimates using Bayesian Causal Forests. estimate_ite_cf function generate ITE estimates using Causal Forests. estimate_ite_ipw function generate ITE estimates using IPW. estimate_ite_or function generate ITE estimates using Outcome Regression. estimate_ite_sipw function generate ITE estimates using SIPW. extract_rules function extract list causal rules randomForest GBM models. generate_rules function generate causal rule models using randomForest GBM methods. generate_rules_matrix function convert list causal rules matrix. select_causal_rules function apply penalized regression causal rules. select important ones. split_data function split input data discovery inference subsamples. take1 function create subsample indices.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-0-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.0.1 (2021-10-20)","text":"seed argument generate_cre_datase function.","code":""}]
+[{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement nkhoshnevis@g.harvard.edu. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"CRE","text":"Installing CRAN. Installing latest developing version. Import.","code":"install.packages(\"CRE\") library(devtools) install_github(\"NSAPH-Software/CRE\", ref = \"develop\") library(\"CRE\")"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"arguments","dir":"Articles","previous_headings":"","what":"Arguments","title":"CRE","text":"Data (required) y observed response/outcome vector (binary continuous). z treatment/exposure/policy vector (binary). X covariate matrix (binary continuous). Parameters (required) method_parameters list parameters define models used, including: - ratio_dis ratio data delegated discovery sub-sample (default: 0.5). - ite_method method estimate individual treatment effect (default: “aipw”) [1]. - learner_ps (SuperLearner) model propensity score estimation (default: “SL.xgboost”, used “aipw”,“bart”,“cf” ITE estimators). - learner_y (SuperLearner) model outcome estimation (default: “SL.xgboost”, used “aipw”,“slearner”,“tlearner” “xlearner” ITE estimators). hyper_params list hyper parameters fine tune method, including: - intervention_vars Intervention-able variables used Rules Generation (default: NULL). - ntrees number decision trees random forest (default: 20). - node_size Minimum size trees’ terminal nodes (default: 20). - max_rules Maximum number candidate decision rules (default: 50). - max_depth Maximum rules length (default: 3). - t_decay decay threshold rules pruning (default: 0.025). - t_ext threshold define generic specific (extreme) rules (default: 0.01). - t_corr threshold define correlated rules (default: 1). - stability_selection Method stability selection selecting rules. vanilla stability selection, error_control stability selection error control stability selection (default: vanilla). - B Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). - subsample Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). - offset Name covariate use offset (.e. “x1”) T-Poisson ITE Estimation. NULL used (default: NULL). - cutoff Threshold defining minimum cutoff value stability scores Stability Selection (default: 0.9). - pfer Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). Additional Estimates (required) ite estimated ITE vector. given, ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"options-for-the-ite-estimation","dir":"Articles","previous_headings":"Arguments > Notes","what":"Options for the ITE estimation","title":"CRE","text":"[1] Options ITE estimation follows: - S-Learner (slearner). - T-Learner (tlearner) - T-Poisson(tpoisson) - X-Learner (xlearner) - Augmented Inverse Probability Weighting (aipw) - Causal Forests (cf) - Causal Bayesian Additive Regression Trees (bart) estimates ITE provided ite additional argument, ITE estimations discovery inference skipped values estimates used instead. ITE estimator requires also outcome learner /propensity score learner SuperLearner package (.e., “SL.lm”, “SL.svm”). models simple classifiers/regressors. default XGBoost algorithm used steps.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"customized-wrapper-for-superlearner","dir":"Articles","previous_headings":"Arguments > Notes","what":"Customized wrapper for SuperLearner","title":"CRE","text":"One can create customized wrapper SuperLearner internal packages. following example providing number cores (e.g., 12) xgboost package shared memory system. use “m_xgboost”, instead “SL.xgboost”.","code":"m_xgboost <- function(nthread = 12, ...) {   SuperLearner::SL.xgboost(nthread = nthread, ...) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/CRE.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"CRE","text":"Example 1 (default parameters) Example 2 (personalized ite estimation) Example 3 (setting parameters) synthetic data sets can generated using generate_cre_dataset().","code":"set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  cre_results <- cre(y, z, X) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\")   y <- dataset[[\"y\"]]   z <- dataset[[\"z\"]]   X <- dataset[[\"X\"]]  ite_pred <- ... # personalized ite estimation cre_results <- cre(y, z, X, ite = ite_pred) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X) set.seed(9687) dataset <- generate_cre_dataset(n = 1000,                                  rho = 0,                                  n_rules = 2,                                  p = 10,                                 effect_size = 2,                                  binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = c(\"x1\",\"x2\",\"x3\",\"x4\"),                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.8,                      pfer = 1,                      B = 10,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"environment-setup","dir":"Articles","previous_headings":"","what":"Environment Setup","title":"Contribution","text":"Please follow steps get copy CRE Github account. Navigate CRE Github repository, top right corner, click Fork button. add clone project Github account. Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): already SSH key, need generate one. Read . Now, can modify codebase track modification. good idea create new branch work codebase. Read following instructions git branching.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"git-branching-model","dir":"Articles","previous_headings":"","what":"Git Branching Model","title":"Contribution","text":"Although, personal repository, can pick branch name, however, order keep consistency also understand working , following convention strongly recommended. project, follow convention proposed Vincent Driessen successful Git branching model post.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"where-to-submit-pull-requests","dir":"Articles","previous_headings":"","what":"Where to submit pull requests?","title":"Contribution","text":"pull requests submitted base repository: fasrc/CRE base: develop branch.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"pull-request-checklist","dir":"Articles","previous_headings":"","what":"Pull request checklist","title":"Contribution","text":"Please run devtools::document(), devtools::load_all() final modifications. Make sure modified code passes checks tests (can run devtools::check() RStudio) PR pass CI reviews can merge . Add line(s) modification NEWS.md file. adding new features, please make sure appropriate documentation added updated. Please clean white spaces. Read .","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"reporting-bugs","dir":"Articles","previous_headings":"","what":"Reporting bugs","title":"Contribution","text":"Please report potential bugs creating new issue sending us email. Please include following information bug report: brief description , expected happen, happened. OS using whether using personal computer HPC cluster. version package installed.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"style-guide","dir":"Articles","previous_headings":"","what":"Style Guide","title":"Contribution","text":"project, follow tidyverse style guide.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"names","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Names","title":"Contribution","text":"File names snake_case ends .R (e.g., create_matching.R) variable names small letter separate _ need (e.g., delta_n) Function names follow snake_case style (e.g., generate_data) Function names follow verb+output convention (e.g., compute_resid)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"spaces-and-indentation","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Spaces and Indentation","title":"Contribution","text":"Indentations two spaces (use tab) Place space around binary operators (e.g., x + y) Place space comma Place space # commenting avoid multiple ### put space opening closing parenthesis Place space () used , , .","code":"#Acceptable: z <- x + y #Not recommended: z<-x+y # (no space) z<- x+y z<-x +y #Acceptable: a <- matrix(c(1:100), nrow = 5) #Not recommended: a <- matrix(c(1:100),nrow = 5) # (no space after comma) a <- matrix( c(1:100), nrow = 5 ) # (extra space after and before parentheses) a<-matrix(c(1:100), nrow = 5) # (no space around unary operator <- ) #Acceptable: # This is a comment #Not recommended: #This is a comment #    This is a comment (more than one space after #) ## This is a comment (multiple #) ###    This is a comment (multiple # and more than one space) #Acceptable: x <- (z + y) #Not recommended: x <- ( z + y ) # (unnecessary space) x <- (z + y ) x <- ( z + y) #Acceptible if (x > 2) {   print(x) } # Not recommended if(x > 2){   print(x) }"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Contribution.html","id":"other-notes","dir":"Articles","previous_headings":"Style Guide > Summary","what":"Other notes","title":"Contribution","text":"Maximum line length 80 character Use explicit returns Use explicit tags documentation (e.g., @title, @description, …)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"getting-the-code","dir":"Articles","previous_headings":"","what":"Getting the code","title":"Testing the CRE Package","text":"test package, please install package system (R (>= 3.5.0)). can install package following one approaches: Directly GitHub CRAN (recommended) Source Forked Repository (recommended)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-directly-from-github","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package directly from Github","title":"Testing the CRE Package","text":"project, follow successful Git Branching Model. result, develop branch updated branch developers. Use devtools::install_github install package. specify ref, install master (main) branch. Try ?CRE. open package description page help tab (assuming using RStudio).","code":"library(devtools) try(detach(\"package:CRE\", unload = TRUE), silent = TRUE) # if already you have the package, detach and unload it, to have a new install.  install_github(\"NSAPH-Software/CRE\", ref=\"develop\") library(CRE)"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-cran","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from CRAN","title":"Testing the CRE Package","text":"Installing package CRAN developing purposes recommended. probably, version CRAN latest version. [Complete section submitting package CRAN]","code":""},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"installing-the-package-from-the-source","dir":"Articles","previous_headings":"Getting the code","what":"Installing the package from the source","title":"Testing the CRE Package","text":"order install package source, need download source code computer install source. steps: Go package Github repository drop-menu change branch develop. click Code tab click Download Zip tab. Open one files using RStudio, change project directory project directory (Session > Set Working Directory > Project Directory). Load devtools library load CRE.","code":"library(devtools) load_all() ?CRE"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"forking-the-package","dir":"Articles","previous_headings":"Getting the code","what":"Forking the package","title":"Testing the CRE Package","text":"Forking package Github account best option planning installing, testing, modifying, contributing project. Go package Github repository click Fork button top right corner. forking package, Open terminal (Gitbash Windows, Anaconda prompt, …) run following command (brackets included): Now, can modify codebase track modification. Navigate package folder Install package following Installing package source steps. good idea create new branch work codebase. Read successful Git Branching Model branching convention.","code":"git clone git@github.com:[your user name]/CRE.git"},{"path":"https://github.com/NSAPH-Software/CRE/articles/Testing-the-Package.html","id":"testing-the-package","dir":"Articles","previous_headings":"","what":"Testing the Package","title":"Testing the CRE Package","text":"Run following command test package.","code":"library(CRE)  # Generate sample data set.seed(1358) dataset <- generate_cre_dataset(n = 1000,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method = \"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\",                       offset = NULL)  hyper_params <- list(intervention_vars = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.01,                      t_corr = 1,                      t_pvalue = 0.05,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 10,                      subsample = 0.5)  # linreg CATE estimation with aipw ITE estimation cre_results <- cre(y, z, X, method_params, hyper_params) summary(cre_results) plot(cre_results) ite_pred <- predict(cre_results, X)"},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Khoshnevis. Author, maintainer.            FASRC Daniela Maria Garcia. Author. Riccardo Cadei. Author. Kwonsang Lee. Author. Falco Joannes Bargagli Stoffi. Author.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cadei R, Khoshnevis N, Lee K, Garcia D, Stoffi F (2023). “CRE: R package interpretable discovery inference heterogeneous treatment effects.” Journal Open Source Software, 8(92), 5587. doi:10.21105/joss.05587, https://doi.org/10.21105/joss.05587.","code":"@Article{,   title = {CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects},   author = {Riccardo Cadei and Naeem Khoshnevis and Kwonsang Lee and Daniela Maria Garcia and Falco J. Bargagli Stoffi},   year = {2023},   journal = {Journal of Open Source Software},   volume = {8},   number = {92},   pages = {5587},   doi = {10.21105/joss.05587},   url = {https://doi.org/10.21105/joss.05587}, }"},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"Identifying subgroups study population treatment exposure notably larger smaller effect outcome compared population average crucial social health sciences. estimating conditional average treatment effect (CATE) given pre-specified set covariates common approach, allows estimating causal effects subgroups specified priori researchers. package implements recently developed Causal Rule Ensemble (CRE) algorithm, flexible stable method interpretable discovery estimation heterogeneous treatment effects terms decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"package-details","dir":"","previous_headings":"","what":"Package Details","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"cre() function executes entire CRE algorithm utilizing data set input parameters user provides. divides study data discovery inference subsamples, generating filtering decision rules discovery subsample explain heterogeneity treatment effects. cre() uses inference subsample estimate linear decomposition Conditional Average Treatment Effect (CATE) terms just discovered decision rules, normal equations model ITE estimates. steps, cre() agnostic concerning method used ITE estimation. estimate Individual Treatment Effects (ITE), users can choose various methodologies vary terms speed accuracy. Several ITE estimators internally implemented (.e. AIPW, Causal Forest, Bayesian Causal Forest, Causal BART, S-Learner, T-Learner, X-Learner), customized ITE estimation can also provided input. results collected S3 object containing: number decision rules extracted step, CATE decomposition estimates corresponding uncertainty quantification, list selected parameters, predicted ITEs.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/index.html","id":"applications","dir":"","previous_headings":"","what":"Applications","title":"Interpretable Subgroups Identification through Ensemble Learning of Causal Rules","text":"CRE package can used diverse theoretical practical applications long working binary exposure treatment level. example, can used education determine teaching methods effective specific student subgroups. marketing, can identify effective strategies different consumer subgroups. healthcare, can used identify patient subgroups benefit particular treatment intervention. can also used studies personalized medicine subgroup patients responds differently provided medicine. public health, CRE method applied discover subgroups vulnerable (resilient) causal effects long-term exposure air pollution mortality.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":null,"dir":"Reference","previous_headings":"","what":"The CRE package — CRE-package","title":"The CRE package — CRE-package","text":"health social sciences, critically important identify subgroups study population treatment notable heterogeneity causal effects respect average treatment effect. Data-driven discovery heterogeneous treatment effects (HTE) via decision tree methods proposed task. Despite high interpretability, single-tree discovery HTE tends highly unstable find oversimplified representation treatment heterogeneity. accommodate shortcomings, propose Causal Rule Ensemble (CRE), new method discover heterogeneous subgroups ensemble--trees approach. CRE following features: provides interpretable representation HTE; 2) allows extensive exploration complex heterogeneity patterns; 3) guarantees high stability discovery. discovered subgroups defined terms interpretable decision rules, develop general two-stage approach subgroup-specific conditional causal effects estimation, providing theoretical guarantees.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The CRE package — CRE-package","text":"Bargagli-Stoffi, F. J., Cadei, R., Lee, K. Dominici, F. (2023). Causal rule ensemble: Interpretable Discovery Inference Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/CRE-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The CRE package — CRE-package","text":"Naeem Khoshnevis Daniela Maria Garcia Riccardo Cadei Kwonsang Lee Falco Joannes Bargagli Stoffi","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Sensitivity Analysis — analyze_sensitivity","title":"Sensitivity Analysis — analyze_sensitivity","text":"Method analyzing sensitivity estimates causal rule-specific treatment effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sensitivity Analysis — analyze_sensitivity","text":"","code":"analyze_sensitivity(ite_std, rules_matrix_std)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sensitivity Analysis — analyze_sensitivity","text":"ite_std standardized ITE rules_matrix_std standardized causal rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/analyze_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sensitivity Analysis — analyze_sensitivity","text":"list containing results sensitivity analysis","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"A helper function for cre object — autoplot.cre","title":"A helper function for cre object — autoplot.cre","text":"helper function plot cre object using ggplot2 package.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A helper function for cre object — autoplot.cre","text":"","code":"# S3 method for cre autoplot(object, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A helper function for cre object — autoplot.cre","text":"object cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/autoplot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A helper function for cre object — autoplot.cre","text":"Returns ggplot object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_hyper_params","title":"Check input parameters — check_hyper_params","text":"Checks consistency input (hyper) parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_hyper_params","text":"","code":"check_hyper_params(X_names, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_hyper_params","text":"X_names observed covariates names. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_hyper_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_hyper_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input parameters — check_input","title":"Check input parameters — check_input","text":"Checks input parameters cre function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input parameters — check_input","text":"","code":"check_input(y, z, X, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input parameters — check_input","text":"y observed response vector. z treatment vector. X covariate matrix. params list parameters required run function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input parameters — check_input","text":"list params might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input data — check_input_data","title":"Check input data — check_input_data","text":"Conducts sanity checks input data.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input data — check_input_data","text":"","code":"check_input_data(y, z, X, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input data — check_input_data","text":"y observed response vector. z treatment vector. X features matrix. ite estimated ITE vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_input_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input data — check_input_data","text":"number data samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check method-related parameters — check_method_params","title":"Check method-related parameters — check_method_params","text":"Checks method-related parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check method-related parameters — check_method_params","text":"","code":"check_method_params(y, ite, params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check method-related parameters — check_method_params","text":"y observed response vector. ite estimated ITE vector. params list parameters required run method functions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/check_method_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check method-related parameters — check_method_params","text":"modified input params. list parameters might changed checks.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Causal rule ensemble — cre","title":"Causal rule ensemble — cre","text":"Performs Causal Rule Ensemble data set response variable, treatment variable, various features.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Causal rule ensemble — cre","text":"","code":"cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Causal rule ensemble — cre","text":"y observed response vector. z treatment vector. X covariate matrix (data frame). provided numerical values. method_params list parameters define models used, including: Parameters Honest Splitting ratio_dis: ratio data delegated rules discovery (default: 0.5). Parameters Discovery Inference ite_method: method ITE (pseudo-outcome) estimation (default: \"aipw\", options: \"aipw\" Augmented Inverse Probability Weighting, \"cf\" Causal Forest, \"bart\" Causal Bayesian Additive Regression Trees, \"slearner\" S-Learner, \"tlearner\" T-Learner, \"xlearner\" X-Learner, \"tpoisson\" T-Learner Poisson regression). learner_ps: model propensity score estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"bart\", \"cf\" ITE estimators). learner_y: model outcome estimation (default: \"SL.xgboost\", options: SuperLearner prediction model .e., \"SL.lm\", \"SL.svm\", used \"aipw\", \"slearner\", \"tlearner\" \"xlearner\" ITE estimators). hyper_params list hyper parameters fine-tune method, including: General hyper parameters intervention_vars: Array intervention-able covariates names used Rules Generation. Empty null array means covariates considered intervention-able (default: NULL). ntrees: number decision trees random forest (default: 20). node_size: Minimum size trees' terminal nodes (default: 20). max_rules: Maximum number generated candidates rules (default: 50). max_depth: Maximum rules length (default: 3). t_decay: decay threshold rules pruning. Higher values carry aggressive pruning (default: 0.025). t_ext: threshold truncate generic specific (extreme) rules (default: 0.01, range: [0, 0.5)). t_corr: threshold define correlated rules (default: 1, range: [0,+inf)). stability_selection: Method stability selection selecting rules. \"vanilla\" stability selection, \"error_control\" stability selection error control  \"\" stability selection (default: \"vanilla\"). B: Number bootstrap samples stability selection rules selection uncertainty quantification estimation (default: 20). subsample: Bootstrap ratio subsample stability selection rules selection uncertainty quantification estimation (default: 0.5). Method specific hyper parameters offset: Name covariate use offset (.e., \"x1\") T-Poisson ITE estimation. Use NULL offset used (default: NULL). cutoff:  Threshold (percentage) defining minimum cutoff value stability scores Stability Selection (default: 0.9). pfer: Upper bound per-family error rate (tolerated amount falsely selected rules) Error Control Stability Selection (default: 1). ite estimated ITE vector. given ITE estimation steps Discovery Inference skipped (default: NULL).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Causal rule ensemble — cre","text":"S3 object composed : M number Decision Rules extracted step, CATE data.frame Conditional Average Treatment Effect decomposition estimates corresponding uncertainty quantification, method_params list method parameters, hyper_params list hyper parameters, rules list rules (implicit form) decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Causal rule ensemble — cre","text":"intervention_vars provided, important note individual treatment effect still computed using covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Causal rule ensemble — cre","text":"","code":"# \\donttest{ set.seed(123) dataset <- generate_cre_dataset(n = 400,                                 rho = 0,                                 n_rules = 2,                                 p = 10,                                 effect_size = 2,                                 binary_covariates = TRUE,                                 binary_outcome = FALSE,                                 confounding = \"no\") y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- dataset[[\"X\"]]  method_params <- list(ratio_dis = 0.5,                       ite_method =\"aipw\",                       learner_ps = \"SL.xgboost\",                       learner_y = \"SL.xgboost\")  hyper_params <- list(intervention_vars = NULL,                      offset = NULL,                      ntrees = 20,                      node_size = 20,                      max_rules = 50,                      max_depth = 3,                      t_decay = 0.025,                      t_ext = 0.025,                      t_corr = 1,                      stability_selection = \"vanilla\",                      cutoff = 0.6,                      pfer = 1,                      B = 20,                      subsample = 0.5)  cre_results <- cre(y, z, X, method_params, hyper_params) #> Loading required package: nnls #> Registered S3 method overwritten by 'randomForest': #>   method      from #>   plot.margin RRF  # }"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":null,"dir":"Reference","previous_headings":"","what":"The Causal Rule Ensemble Crossfit — cre_crossfit","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"Performs Causal Rule Ensemble twice dataset response variable, treatment variable, various features, swapping discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"cre_crossfit(   y,   z,   X,   ite_method_dis,   include_ps_dis = NA,   ps_method_dis = \"SL.xgboost\",   or_method_dis = NA,   ite_method_inf,   include_ps_inf = NA,   ps_method_inf = \"SL.xgboost\",   or_method_inf = NA,   ntrees_rf,   ntrees_gbm,   min_nodes,   max_nodes,   t,   q,   rules_method = NA,   include_offset = FALSE,   offset_name = NA,   cate_method = \"DRLearner\",   cate_SL_library = \"SL.xgboost\",   filter_cate = FALSE )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"y observed response vector z treatment vector X covariate matrix ite_method_dis method estimate discovery sample ITE include_ps_dis whether include propensity score estimate covariate discovery ITE estimation, considered BART, XBART, CF ps_method_dis estimation model propensity score discovery subsample or_method_dis estimation model outcome regressions estimate_ite_aipw discovery subsample ite_method_inf method estimate inference sample ITE include_ps_inf whether include propensity score estimate covariate inference ITE estimation, considered BART, XBART, CF ps_method_inf estimation model propensity score inference subsample or_method_inf estimation model outcome regressions estimate_ite_aipw inference subsample ntrees_rf number decision trees randomForest ntrees_gbm number decision trees gradient boosting min_nodes minimum size trees' terminal nodes max_nodes maximum size trees' terminal nodes t common support used generating causal rules matrix q selection threshold used selecting causal rules rules_method method selecting causal rules binary outcomes include_offset whether include offset estimating ITE, poisson offset_name name offset, included cate_method method estimate CATE values cate_SL_library library used cate_method set DRLearner filter_cate whether filter rules p-value <= 0.05","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"S3 object containing matrix averaged Conditional Average Treatment Effect estimates two CRE iterations","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/cre_crossfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Causal Rule Ensemble Crossfit — cre_crossfit","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  cre_cross_results <- cre_crossfit(y = dataset[[\"y\"]], z = dataset[[\"z\"]],                                   X = as.data.frame(dataset[[\"X\"]]),                                   ite_method_dis = \"bart\",                                   include_ps_dis = TRUE,                                   ite_method_inf = \"bart\",                                   include_ps_inf = TRUE,                                   ntrees_rf = 100, ntrees_gbm = 50,                                   min_nodes = 20, max_nodes = 5,                                   t = 0.025, q = 0.8) #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> fitting treatment model via method 'bart' #> fitting response model via method 'bart' #> Conducting Discovery Subsample Analysis 1 #> 411 rules (length<=15) were extracted from the first 100 trees. #> 150 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 1 #> Conducting Discovery Subsample Analysis 2 #> 493 rules (length<=15) were extracted from the first 100 trees. #> 100 rules (length<=15) were extracted from the first 50 trees. #> Conducting Inference Subsample Analysis 2"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"Discard rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"","code":"discard_anomalous_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_anomalous_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Anomalous (Causal) Decision Rules — discard_anomalous_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"Discard redundant correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"","code":"discard_correlated_rules(rules_matrix, rules_list, t)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"rules_matrix causal rules matrix. rules_list list causal rules. t threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discard_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard Correlated (Causal) Decision Rules — discard_correlated_rules","text":"list : filtered rules matrix filtered rules list","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover causal decision rules — discover_causal_rules","title":"Discover causal decision rules — discover_causal_rules","text":"Discovers Causal Decision Rules randomization-based tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover causal decision rules — discover_causal_rules","text":"","code":"discover_causal_rules(   rules_matrix,   rules_list,   ite,   stability_selection,   cutoff,   pfer,   penalty_rl )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover causal decision rules — discover_causal_rules","text":"rules_matrix causal rules matrix. rules_list vector causal rules. ite estimated ITE. stability_selection Whether using stability selection select causal rules. cutoff Threshold (percentage) defining minimum cutoff value stability scores. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). penalty_rl Order penalty rules length LASSO Causal Rules Discovery (.e. 0: penalty, 1: rules_length, 2: rules_length^2)","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover causal decision rules — discover_causal_rules","text":"List Causal Decision Rules discovered","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Discover rules — discover_rules","title":"Discover rules — discover_rules","text":"Discover minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discover rules — discover_rules","text":"","code":"discover_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discover rules — discover_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/discover_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discover rules — discover_rules","text":"minimal set rules linearly decomposing Conditional Average Treatment Effect (CATE).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Conditional Average Treatment Effect — estimate_cate","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"Estimates Conditional Average Treatment Effect (CATE) linearly modeling Individual Treatment Effect set rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"","code":"estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"rules_matrix rules matrix. rules_explicit list select rules terms covariate names. ite estimated ITEs. B number bootstrap samples uncertainty quantification estimation. subsample bootstrap ratio subsample uncertainty quantification estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_cate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Conditional Average Treatment Effect — estimate_cate","text":"list 2 elements: summary: data frame summarizing CATE linear decomposition: Rule: rule name, Estimate: linear contribution CATE, CI_lower: lower bound 95% confidence interval estimate, CI_upper: upper bound 95% confidence interval estimate, P_Value: p-value (Z-test). model: linear model CATE-ATE estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"Estimates Individual Treatment Effect given response vector, treatment vector, covariate matrix, desired algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"","code":"estimate_ite(y, z, X, ite_method, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"y observed response vector. z observed treatment vector. X covariate matrix. ite_method method estimating Individual Treatment Effect. methods requires additional parameters. parameters mentioned indented blocks method definitions provided end parameters list. \"slearner\": S-Learner. - learner_y \"tlearner\": T-Learner. learner_y \"tpoisson\": T-Poisson. offset \"xlearner\": X-Learner. learner_y \"aipw\": Augmented Inverse Probability Weighting. learner_ps  learner_y \"bart\": Bayesian Additive Regression Trees. learner_ps \"cf\": Causal Forest. learner_ps ... Additional parameters passed different models.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"list ITE estimates.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite.html","id":"additional-parameters","dir":"Reference","previous_headings":"","what":"Additional parameters","title":"Estimate the Individual Treatment Effect (ITE) — estimate_ite","text":"learner_ps: estimation method propensity score. includes libraries SuperLearner package. learner_y: estimation model outcome. includes libraries SuperLearner package. offset: Name covariate use offset (.e. \"x1\") Poisson ITE Estimation. NULL offset used.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"Estimates Individual Treatment Effect using Augmented Inverse Probability Weighting given response vector, treatment vector, features matrix, estimation model propensity score estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"","code":"estimate_ite_aipw(y, z, X, learner_ps = \"SL.xgboost\", learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"y observed response vector. z treatment vector. X features matrix. learner_ps estimation model propensity score. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_aipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Augmented Inverse\nProbability Weighting (AIPW) — estimate_ite_aipw","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"Estimates Individual Treatment Effect using Bayesian Additive Regression Trees given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"","code":"estimate_ite_bart(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"y observed response vector. z treatment vector. X features matrix. learner_ps Method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bart.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Additive\nRegression Trees (BART) — estimate_ite_bart","text":"number samples number burn set default equal 500.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"Estimates Individual Treatment Effect using Bayesian Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"","code":"estimate_ite_bcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"y observed response vector. z treatment vector. X features matrix. ps_method method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_bcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Bayesian Causal Forest\n(BCF) — estimate_ite_bcf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"Estimates Individual Treatment Effect using Causal Forest given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"","code":"estimate_ite_cf(y, z, X, learner_ps)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"y observed response vector. z treatment vector. X features matrix. learner_ps method estimation propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_cf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF) — estimate_ite_cf","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"Estimates Individual Treatment Effect (ITE) using Inverse Propensity Weighting (IPW) given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"","code":"estimate_ite_ipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_ipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Inverse Propensity\nWeighting (IPW) — estimate_ite_ipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"Method estimating Individual Treatment Effect using Outcome Regression given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"estimate_ite_or(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"y observed response vector z treatment vector X features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Individual Treatment Effect using Outcome Regression — estimate_ite_or","text":"","code":"dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary = FALSE)  # Initialize parameters y <- dataset[[\"y\"]] z <- dataset[[\"z\"]] X <- as.data.frame(dataset[[\"X\"]])  ite_list <- estimate_ite_or(y, z, X) #> *****Into main of wbart #> *****Data: #> data:n,p,np: 388, 10, 612 #> y1,yn: -0.694294, 1.362455 #> x1,x[n*p]: 1.000000, 1.000000 #> xp1,xp[np*p]: 0.000000, 0.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.118817,3.000000,0.250422 #> *****sigma: 1.133838 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000 #> *****Into main of wbart #> *****Data: #> data:n,p,np: 612, 10, 388 #> y1,yn: -0.485955, 0.857738 #> x1,x[n*p]: 0.000000, 0.000000 #> xp1,xp[np*p]: 1.000000, 1.000000 #> *****Number of Trees: 200 #> *****Number of Cut Points: 1 ... 1 #> *****burn and ndpost: 100, 1000 #> *****Prior:beta,alpha,tau,nu,lambda: 2.000000,0.950000,0.139249,3.000000,0.168737 #> *****sigma: 0.930723 #> *****w (weights): 1.000000 ... 1.000000 #> *****Dirichlet:sparse,theta,omega,a,b,rho,augment: 0,0,1,0.5,1,10,0 #> *****nkeeptrain,nkeeptest,nkeeptestme,nkeeptreedraws: 1000,1000,1000,1000 #> *****printevery: 100 #> *****skiptr,skipte,skipteme,skiptreedraws: 1,1,1,1 #>  #> MCMC #> done 0 (out of 1100) #> done 100 (out of 1100) #> done 200 (out of 1100) #> done 300 (out of 1100) #> done 400 (out of 1100) #> done 500 (out of 1100) #> done 600 (out of 1100) #> done 700 (out of 1100) #> done 800 (out of 1100) #> done 900 (out of 1100) #> done 1000 (out of 1100) #> time: 6s #> check counts #> trcnt,tecnt,temecnt,treedrawscnt: 1000,1000,1000,1000"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"Estimates Individual Treatment Effect (ITE) using Outcome Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"","code":"estimate_ite_oreg(y, z, X)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"y observed response vector. z treatment vector. X features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_oreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Outcome Regression\n(OREG) — estimate_ite_oreg","text":"vector ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"Estimates Individual Treatment Effect using Poisson Regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"","code":"estimate_ite_poisson(y, z, X, X_names, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"y observed response vector. z treatment vector. X features matrix. X_names vector includes names covariates. offset name covariate use offset (.e. 'x1') model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Poisson Regression — estimate_ite_poisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"Estimates Individual Treatment Effect using Stabilized Inverse Propensity Weighting given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"","code":"estimate_ite_sipw(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"y observed response vector. z treatment vector. X features matrix. ps_method estimation method propensity score.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_sipw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using Stabilized Inverse\nPropensity Weighting (SIPW) — estimate_ite_sipw","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"Estimates Individual Treatment Effect using S-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"","code":"estimate_ite_slearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_slearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using S-Learner — estimate_ite_slearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"Estimates Individual Treatment Effect using T-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"","code":"estimate_ite_tlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Learner — estimate_ite_tlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"Estimates Individual Treatment Effect using Poisson regression given response vector, treatment vector, features matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"","code":"estimate_ite_tpoisson(y, z, X, offset)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"y observed response vector. z treatment vector. X features matrix. offset name covariate use offset (.e. “x1”) model corresponding outcome rate. NULL model directly outcome counts without offset.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_tpoisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using T-Poisson regression — estimate_ite_tpoisson","text":"vector ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"","code":"estimate_ite_xbart(y, z, X, include_ps, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"y observed response vector z treatment vector X features matrix include_ps whether include propensity score estimate covariate ITE estimation ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Additive Regression Trees — estimate_ite_xbart","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"Method estimating Individual Treatment Effect using Accelerated Bayesian Causal Forest given response vector, treatment vector, features matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"","code":"estimate_ite_xbcf(y, z, X, ps_method)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"y observed response vector z treatment vector X features matrix ps_method estimation method propensity score","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xbcf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect using Accelerated Bayesian Causal Forest — estimate_ite_xbcf","text":"list ITE estimates standard deviations ITE estimates","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"Estimates Individual Treatment Effect using X-Learner given response vector, treatment vector, features matrix estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"","code":"estimate_ite_xlearner(y, z, X, learner_y = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"y observed response vector. z treatment vector. X features matrix. learner_y estimation model outcome.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ite_xlearner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Individual Treatment Effect (ITE) using X-Learner — estimate_ite_xlearner","text":"list ITE estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the propensity score — estimate_ps","title":"Estimate the propensity score — estimate_ps","text":"Estimates Propensity Score given treatment vector features data frame.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the propensity score — estimate_ps","text":"","code":"estimate_ps(z, X, ps_method = \"SL.xgboost\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the propensity score — estimate_ps","text":"z treatment vector. X features data frame. ps_method estimation model propensity score (default: SL.xgboost).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/estimate_ps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the propensity score — estimate_ps","text":"vector propensity score estimates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":null,"dir":"Reference","previous_headings":"","what":"Discovery (performance) evaluation — evaluate","title":"Discovery (performance) evaluation — evaluate","text":"Computes different metrics discovery evaluation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discovery (performance) evaluation — evaluate","text":"","code":"evaluate(ground_truth, prediction)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discovery (performance) evaluation — evaluate","text":"ground_truth List true (CDR/EM) values. prediction List predicted (CDR/EM) values.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/evaluate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discovery (performance) evaluation — evaluate","text":"Intersection union, precision, recall.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract effect modifiers — extract_effect_modifiers","title":"Extract effect modifiers — extract_effect_modifiers","text":"Extracts effect modifiers list (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract effect modifiers — extract_effect_modifiers","text":"","code":"extract_effect_modifiers(rules_list, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract effect modifiers — extract_effect_modifiers","text":"rules_list list (causal) decision rules. X_names list covariate names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_effect_modifiers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract effect modifiers — extract_effect_modifiers","text":"list effect modifiers.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract (causal) decision rules — extract_rules","title":"Extract (causal) decision rules — extract_rules","text":"Extracts causal rules random forest gradient boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract (causal) decision rules — extract_rules","text":"","code":"extract_rules(treelist, X, max_depth, digits = 2)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract (causal) decision rules — extract_rules","text":"treelist list decision trees. X Features matrix. max_depth number top levels tree considered. digits Number digits rounding decision rules extract conditions.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/extract_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract (causal) decision rules — extract_rules","text":"vector (causal) decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter correlated rules — filter_correlated_rules","title":"Filter correlated rules — filter_correlated_rules","text":"Discards highly correlated rules (.e., \\(Cov(rule_1,rule_2) > t_{corr}\\)).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter correlated rules — filter_correlated_rules","text":"","code":"filter_correlated_rules(rules_matrix, rules_list, t_corr)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter correlated rules — filter_correlated_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_corr threshold define correlated rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_correlated_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter correlated rules — filter_correlated_rules","text":"rules matrix without highly correlated rules (columns).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter extreme decision rules — filter_extreme_rules","title":"Filter extreme decision rules — filter_extreme_rules","text":"Discards rules many observations.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter extreme decision rules — filter_extreme_rules","text":"","code":"filter_extreme_rules(rules_matrix, rules_list, t_ext)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules_matrix rules matrix. rules_list list rules (names). t_ext threshold [0, 0.5) range truncate generic specific rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_extreme_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter extreme decision rules — filter_extreme_rules","text":"rules matrix without rare/common rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"Filters irrelevant decision rules. irrelevant rules interpreted error increase removing variable-value pair decision rules (see “Interpreting tree ensembles inTrees package” Houtao Deng, 2019).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"","code":"filter_irrelevant_rules(rules, X, ite, t_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"rules list rules. X features matrix. ite estimated ITE. t_decay decay threshold rules pruning.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/filter_irrelevant_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules","text":"list “relevant” rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate causal rules — generate_causal_rules","title":"Generate causal rules — generate_causal_rules","text":"Generates causal decision rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate causal rules — generate_causal_rules","text":"","code":"generate_causal_rules(X, ite, method_params, hyper_params)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate causal rules — generate_causal_rules","text":"X covariate matrix. ite estimated ITE. method_params vector method parameters. hyper_params vector hyper parameters.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate causal rules — generate_causal_rules","text":"vector causal rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate CRE synthetic data — generate_cre_dataset","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Generates synthetic data sets run simulation causal inference experiments composed outcome vector (y), treatment vector (z), covariates matrix (X), unobserved individual treatment effects vector (ite). arguments specify data set characteristic, including number individuals (n), number covariates (p), correlation within covariates (rho),  number decision rules (n_rules) decomposing Conditional Average Treatment Effect (CATE), treatment effect magnitude (effect_size), confounding mechanism (confounding), whether covariates outcomes binary continuous (binary_covariates, binary_outcome).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"generate_cre_dataset(   n = 1000,   rho = 0,   n_rules = 2,   p = 10,   effect_size = 2,   binary_covariates = TRUE,   binary_outcome = TRUE,   confounding = \"no\" )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate CRE synthetic data — generate_cre_dataset","text":"n integer number represents number observations. Non-integer values converted integer number. rho positive double number represents correlation within covariates (default: 0, range: [0,1)). n_rules number causal rules (default: 2, range: {1,2,3,4}). p number covariates (default: 10). effect_size treatment effect size magnitude (default: 2, range: \\(\\geq\\)0). binary_covariates Whether use binary continuous covariates (default: TRUE). binary_outcome Whether use binary continuous outcomes (default: TRUE). confounding continuous outcome, add confounding variables: \"lin\" linear confounding, \"nonlin\" non-linear confounding, \"\" confounding (default).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate CRE synthetic data — generate_cre_dataset","text":"list, representing generated synthetic data set, containing: y outcome vector, z treatment vector, X covariates matrix, ite individual treatment vector.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate CRE synthetic data — generate_cre_dataset","text":"covariates matrix generated specified correlation among individuals, covariate sampled either Bernoulli(0.5) binary, Gaussian(0,1) continuous. treatment vector sampled Bernoulli(\\(\\frac{1}{1+ \\exp(1-x_1+x_2-x_3)}\\)), enforcing treatment assignment probabilities function observed covariates. potential outcomes (\\(y(0)\\) \\(y(1)\\)) sampled Bernoulli binary, Gaussian (standard deviation equal 1) continuous. mean equal confounding term (null, linear non-linear always null binary outcome) plus 1-4 decision rules weighted treatment effect magnitude. two potential outcomes characterizes CATE (unobserved individual treatment effects vector) sum different additive contributions decision rules considered (plus intercept). final expression CATE depends treatment effect magnitude number decision rules considered. 4 decision rules : Rule 1: \\(1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x})\\) Rule 2: \\(1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})\\) Rule 3: \\(1\\{x_4 \\leq 0.5\\}(\\textbf{x})\\) Rule 4: \\(1\\{x_5 \\leq 0.5; x_7 > 0.5; x_8 \\leq 0.5\\}(\\textbf{x})\\) corresponding additive average treatment effect (AATE) equal : Rule 1: \\(-\\) effect_size, Rule 2: \\(+\\) effect_size, Rule 3: \\(- 0.5 \\cdot\\) effect_size, Rule 4: \\(+ 2 \\cdot\\) effect_size. example, setting effect_size=4 n_rules=2: $$\\text{CATE}(\\textbf{x}) = -4 \\cdot 1\\{x_1 > 0.5; x_2 \\leq 0.5\\}(\\textbf{x}) + 4 \\cdot 1\\{x_5 > 0.5; x_6 \\leq 0.5\\}(\\textbf{x})$$ final outcome vector y finally computed combining potential outcomes according treatment assignment.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate CRE synthetic data — generate_cre_dataset","text":"Set covariates domain (binary_covariates) outcome domain (binary_outcome) according experiment interest. Increase complexity heterogeneity discovery: decreasing sample size (n), adding correlation among covariates (rho), increasing number rules (n_rules), increasing number covariates (p), decreasing absolute value causal effect (effect_size), adding linear -linear confounders (confounding).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_cre_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate CRE synthetic data — generate_cre_dataset","text":"","code":"set.seed(123) dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,                                 effect_size = 2, binary_covariates = TRUE,                                 binary_outcome = TRUE, confounding = \"no\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules — generate_rules","title":"Generate rules — generate_rules","text":"Generates list rules characterizing heterogeneity Conditional Average Treatment Effect (CATE) tree-based methods (.e., random forest).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules — generate_rules","text":"","code":"generate_rules(X, ite, ntrees, node_size, max_rules, max_depth)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules — generate_rules","text":"X covariate matrix. ite vector estimated ITE. ntrees number decision trees random forest algorithm. node_size Minimum size trees' terminal nodes. max_rules Maximum number candidate decision rules. max_depth Maximum rules length.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules — generate_rules","text":"list rules (names).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate rules matrix — generate_rules_matrix","title":"Generate rules matrix — generate_rules_matrix","text":"Generates rules matrix feature covariate matrix vector rules. number rows rules_matrix equal number samples X, number columns equal number rules rules_list. element rules_matrix corresponds specific data sample rule. data sample satisfies rule, corresponding element rules_matrix set 1. Otherwise, element set 0.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate rules matrix — generate_rules_matrix","text":"","code":"generate_rules_matrix(X, rules_list)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate rules matrix — generate_rules_matrix","text":"X Features matrix. rules_list vector rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/generate_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate rules matrix — generate_rules_matrix","text":"causal rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Logger settings — get_logger","title":"Get Logger settings — get_logger","text":"Returns current logger settings.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Logger settings — get_logger","text":"","code":"get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Logger settings — get_logger","text":"Returns list includes logger_file_path logger_level.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/reference/get_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Logger settings — get_logger","text":"","code":"set_logger(\"mylogger.log\", \"INFO\") log_meta <- get_logger()"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":null,"dir":"Reference","previous_headings":"","what":"Honest splitting — honest_splitting","title":"Honest splitting — honest_splitting","text":"Splits data discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Honest splitting — honest_splitting","text":"","code":"honest_splitting(y, z, X, ratio_dis, ite = NULL)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Honest splitting — honest_splitting","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis double number indicating ratio data delegated discovery sub-sample. ite vector estimated ITE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/honest_splitting.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Honest splitting — honest_splitting","text":"list containing discovery inference sub-samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret rules — interpret_rules","title":"Interpret rules — interpret_rules","text":"Replaces column numbers rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret rules — interpret_rules","text":"","code":"interpret_rules(rules, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret rules — interpret_rules","text":"rules vector rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret rules — interpret_rules","text":"list explicit (human-readable) rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret select rules — interpret_select_rules","title":"Interpret select rules — interpret_select_rules","text":"Replaces column numbers select rules vector real names.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret select rules — interpret_select_rules","text":"","code":"interpret_select_rules(select_rules_dis, X_names)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret select rules — interpret_select_rules","text":"select_rules_dis vector select causal rules. X_names vector real names covariates.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/interpret_select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret select rules — interpret_select_rules","text":"vector select causal rules interpretable.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"","code":"lasso_rules_filter(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q Number (unique) selected rules per subsample stability selection. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/lasso_rules_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter (Causal) Decision Rules by LASSO (with stability selection) — lasso_rules_filter","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend generic plot functions for cre class — plot.cre","title":"Extend generic plot functions for cre class — plot.cre","text":"wrapper function extend generic plot functions cre class.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend generic plot functions for cre class — plot.cre","text":"","code":"# S3 method for cre plot(x, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend generic plot functions for cre class — plot.cre","text":"x cre object. ... Additional arguments passed customize plot.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/plot.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend generic plot functions for cre class — plot.cre","text":"Returns ggplot2 object, invisibly. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict individual treatment effect via causal rule ensemble — predict.cre","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"Predicts individual treatment effect via causal rule ensemble algorithm.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"","code":"# S3 method for cre predict(object, X, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"object cre object running CRE function. X covariate matrix (data.frame) ... Additional arguments passed customize prediction.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/predict.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict individual treatment effect via causal rule ensemble — predict.cre","text":"array estimated Individual Treatment Effects","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend print function for the CRE object — print.cre","title":"Extend print function for the CRE object — print.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend print function for the CRE object — print.cre","text":"","code":"# S3 method for cre print(x, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend print function for the CRE object — print.cre","text":"x cre object running CRE function. verbose Set level results description details: 0 results summary, 1 results parameters summary, 2 results parameters rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/print.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extend print function for the CRE object — print.cre","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune (Causal) Decision Rules — prune_rules","title":"Prune (Causal) Decision Rules — prune_rules","text":"Method pruning (Causal) Decision Rules extracted Random Forest Gradient Boosting algorithms.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune (Causal) Decision Rules — prune_rules","text":"","code":"prune_rules(rules, X, ite_std, max_decay, type_decay)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune (Causal) Decision Rules — prune_rules","text":"rules list decision rules. X features matrix. ite_std standardized ITE. max_decay Decay Threshold pruning rules. type_decay Decay Type pruning rules (1: relative error; 2: error).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/prune_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune (Causal) Decision Rules — prune_rules","text":"list (Causal) Decision Rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select Causal Rules — select_causal_rules","title":"Select Causal Rules — select_causal_rules","text":"Selects causal rules important.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select Causal Rules — select_causal_rules","text":"","code":"select_causal_rules(   rules_matrix_std,   rules_list,   ite_std,   q,   stability_selection,   pfer_val )"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select Causal Rules — select_causal_rules","text":"rules_matrix_std standardized causal rules matrix. rules_list vector causal rules. ite_std standardized ITE. q selection threshold used selecting causal rules. stability_selection Whether using stability selection select causal rules. pfer_val Per-Family Error Rate, expected number false discoveries.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_causal_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select Causal Rules — select_causal_rules","text":"vector causal rules","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Select rules — select_rules","title":"Select rules — select_rules","text":"Given set rules, selects minimal set linearly decomposing Conditional Average Treatment Effect (CATE) LASSO (optionally Stability Selection).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select rules — select_rules","text":"","code":"select_rules(rules_matrix, rules, ite, stability_selection, cutoff, pfer, B)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select rules — select_rules","text":"rules_matrix rules matrix. rules list rules. ite estimated ITE. stability_selection Stability selection method. cutoff Threshold (percentage) defining minimum cutoff value stability scores. stability selection. pfer Upper bound per-family error rate (tolerated amount falsely selected rules). stability selection. B Number bootstrap samples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/select_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select rules — select_rules","text":"minimal set rules linearly decomposing CATE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Logger settings — set_logger","title":"Set Logger settings — set_logger","text":"Updates logger settings, including log level location file.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Logger settings — set_logger","text":"","code":"set_logger(logger_file_path = \"CRE.log\", logger_level = \"INFO\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Logger settings — set_logger","text":"logger_file_path path (including file name) log messages. (Default: CRE.log) logger_level log level. log level set, log levels also activated (implemented). Available levels include: TRACE: Provides verbose detailed logging, including steps taken achieve result, often used debugging. Activating TRACE also enable DEBUG, INFO, SUCCESS, WARN, ERROR, FATAL logs. DEBUG: Provides detailed logging flow application, used mostly developers understand potential issues. Activating DEBUG also enable INFO, SUCCESS, WARN, ERROR, FATAL logs. INFO (Default): Standard messages inform user normal operation system. Activating INFO also enable SUCCESS, WARN, ERROR, FATAL logs. SUCCESS: Messages indicating successful completion particular operation task. Activating SUCCESS also enable WARN, ERROR, FATAL logs. WARN: Warning messages events might cause problems future, yet errors. Activating WARN also enable ERROR FATAL logs. ERROR: Reports error due system may able achieve functionality, application halt. Activating ERROR also enable FATAL logs. FATAL: Reports severe error events presumably lead application abort.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Logger settings — set_logger","text":"return value. function called side effects.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set Logger settings — set_logger","text":"Log levels specified developers initial implementation. Future developers contributors can leverage log levels better capture document application's processes events.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/set_logger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Logger settings — set_logger","text":"","code":"set_logger(\"Debug\")"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Data — split_data","title":"Split Data — split_data","text":"Splits data discovery inference subsamples.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Data — split_data","text":"","code":"split_data(y, z, X, ratio_dis)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Data — split_data","text":"y observed response vector. z treatment vector. X features matrix. ratio_dis ratio data delegated discovery subsample","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Data — split_data","text":"list containing discovery inference subsamples","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardize Rules Matrix — standardize_rules_matrix","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardize (.e. mean=0 stdev=1) rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"","code":"standardize_rules_matrix(rules_matrix)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"rules_matrix rules matrix.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/standardize_rules_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardize Rules Matrix — standardize_rules_matrix","text":"Standardized rules matrix","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":null,"dir":"Reference","previous_headings":"","what":"Print summary of CRE object — summary.cre","title":"Print summary of CRE object — summary.cre","text":"Prints brief summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print summary of CRE object — summary.cre","text":"","code":"# S3 method for cre summary(object, verbose = 2, ...)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print summary of CRE object — summary.cre","text":"object cre object running CRE function. verbose Set level results description details: results summary 0, results+parameters summary 1, results+parameters+rules summary (default 2). ... Additional arguments passed customize results description.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/summary.cre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print summary of CRE object — summary.cre","text":"summary CRE object","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":null,"dir":"Reference","previous_headings":"","what":"Take 1 — take1","title":"Take 1 — take1","text":"Generate list indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take 1 — take1","text":"","code":"take1(len)"},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take 1 — take1","text":"len length value","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take 1 — take1","text":"vector indices","code":""},{"path":"https://github.com/NSAPH-Software/CRE/reference/take1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take 1 — take1","text":"","code":"ind <- CRE:::take1(10)"},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-025-2023-12-6","dir":"Changelog","previous_headings":"","what":"CRE 0.2.5 (2023-12-6)","title":"CRE 0.2.5 (2023-12-6)","text":"CRAN release: 2023-12-06","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-5","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.5 (2023-12-6)","text":"Add (vanilla) Stability Selection (without Error Control). max_rules hyper parameters max rules filtering. Uncertainty Quantification estimation bootstrapping. B hyper-parameter, subsample hyper-parameter. rules(implicit form) cre() function return. predict() function ITE estimation via CRE.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-5","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.5 (2023-12-6)","text":"Type stability_selection binary -> string (‘’,‘vanilla’,‘error_control’). Unify ntrees_gbm hyper-parameter ntrees_gbm hyper-parameter ntrees hyper-parameter. rules generation retrieve decision rules also internal nodes, just terminal nodes. ite_method_dis, ite_method_inf method-parameter -> ite_method. ps_method_dis, ps_method_inf method-parameter -> learner_ps. oreg_method_dis, oreg_method_inf method-parameter -> learner_y.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-5","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.5 (2023-12-6)","text":"max_nodes hyper-parameter. Remove rules generation Generalized Boosted Regression. replace hyper-parameter. penalty_rl hyper-parameter. t_pvalue hyper-parameter. ite_pred cre() function return.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-5","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.5 (2023-12-6)","text":"Error saving covariates name CRE result using intervention_vars.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-024-2023-6-14","dir":"Changelog","previous_headings":"","what":"CRE 0.2.4 (2023-6-14)","title":"CRE 0.2.4 (2023-6-14)","text":"CRAN release: 2023-06-14","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-4","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.4 (2023-6-14)","text":"Method paper description updated.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-023-2023-4-27","dir":"Changelog","previous_headings":"","what":"CRE 0.2.3 (2023-4-27)","title":"CRE 0.2.3 (2023-4-27)","text":"CRAN release: 2023-04-27","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-3","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.3 (2023-4-27)","text":"Bayesian Causal Forest (bcf) ITE estimator supported.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-022-2023-4-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.2 (2023-4-17)","title":"CRE 0.2.2 (2023-4-17)","text":"CRAN release: 2023-04-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-2","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.2 (2023-4-17)","text":"Fixed failing unit tests specific operating systems.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-021-2023-3-17","dir":"Changelog","previous_headings":"","what":"CRE 0.2.1 (2023-3-17)","title":"CRE 0.2.1 (2023-3-17)","text":"CRAN release: 2023-03-17","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.1 (2023-3-17)","text":"Replace BATE ATE CATE Linear Decomposition. Update plot() function (remove ATE, old BATE, explicit AATEs).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.1 (2023-3-17)","text":"Code Conduct.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.1 (2023-3-17)","text":"Causal Tree benchmark functional tests.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.1 (2023-3-17)","text":"Rank-Deficient Rule Matrix Issue (redundant rules). Intervention Variables Filtering (ordered filtering).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-020-2023-1-19","dir":"Changelog","previous_headings":"","what":"CRE 0.2.0 (2023-1-19)","title":"CRE 0.2.0 (2023-1-19)","text":"CRAN release: 2023-01-19","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.2.0 (2023-1-19)","text":"offset method-parameter -> hyper-parameter estimate_ite_poisson function -> estimate_ite_tpoisson max_dacay hyper-parameter -> t_decay. interpret_select_rules function -> interpret_rules. generate_causal_rules function -> discover_rules. discover_causal_rules function ->select_rules. offset_name method parameter -> offset. Hyper method parameters required arguments cre. cre object: added parameters ite estimation.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-2-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.2.0 (2023-1-19)","text":"Synthetic data set 1 3 rules (generate_cre_dataset). S-Learner (slearner) method ITE estimation. T-Learner (tlearner) method ITE estimation. X-Learner (xlearner) method ITE estimation. Rules Selection description summary.cre. verbose parameter summary.cre. ite, additional cre input parameter use personalized ite estimations. Default values hyper parameters. Default values method parameters. Simulation experiments estimation (estimation.R). Simulation experiments discovery (discovery.R). extract_effect_modifiers function (utility performance evaluation). evaluate function discovery evaluation. confounding parameter generate_cre_dataset set confounding type. ite_pred model CRE results. binary_covariates parameter generate_cre_dataset set covariates domain.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-2-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.2.0 (2023-1-19)","text":"include_ps_inf method-parameter. include_ps_dis method-parameter. oreg method ITE estimation. ipw method ITE estimation. sipw method ITE estimation. ITE standard deviation estimation. type_decay hyper-parameter. Keep linreg CATE estimation (remove cate_method cate_SL_library parameters). method_params hyper_params additional parameters summary.cre. ite standardization Rules Generation. random_state parameter. include_offset method parameter.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.2.0 (2023-1-19)","text":"Rules Generation Issue (set rules length fix bootstrapping).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-011-2022-10-18","dir":"Changelog","previous_headings":"","what":"CRE 0.1.1 (2022-10-18)","title":"CRE 0.1.1 (2022-10-18)","text":"CRAN release: 2022-10-22","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.1 (2022-10-18)","text":"binary parameter generate_cre_dataset -> binary_outcome . filter_cate hyper-parameter -> t_pvalue. t_anom hyper-parameter -> t_ext. effect_modifier hyper-parameter -> intervention_vars. lasso_rules_filter function -> discover_causal_rules. split_data function -> honest_splitting. prune_rules function -> `filter_irrelevant_rules. discard_correlated_rules function -> filter_correlated_rules. discard_anomalous_rules function -> filter_extreme_rules.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.1 (2022-10-18)","text":"Weighted LASSO Causal Rules Discovery (penalty_rl hyper-parameter).","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"cre-010-2022-10-17","dir":"Changelog","previous_headings":"","what":"CRE 0.1.0 (2022-10-17)","title":"CRE 0.1.0 (2022-10-17)","text":"CRAN release: 2022-10-18","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.1.0 (2022-10-17)","text":"Update examples tests functions. q hyper-parameter -> cutoff. pfer_val hyper-parameter -> pfer. select_causal_rules function -> lasso_rules_filter. t hyper-parameter -> t_anom. Separate standardization, remove filtering generate_rules_matrix function. summary.cre function describe results. min_nodes hyper-parameter -> node_size (randomForest convention). cre returns S3 object.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-1-0","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.1.0 (2022-10-17)","text":"Examples tests functions. prune_rules function discard un-predictive rules. discard_anomalous_rules function discard anomalous rules (see t_corr hyper-parameter.). discard_correlated_rules function discard correlated rules (see t_anom hyper-parameter). effect_modifiers parameter generate_rules function covariates filtering. generate_causal_rules function. Helper function SuperLearner package propensity score estimation estimate_ite_xyz. Five methods CATE estimation (poisson, DRLearner, bart-baggr, cf-means, linreg) estimate_cate function. (ps_method_dis, ps_method_inf, or_method_dis, or_method_inf, cate_SL_library) method-parameters complement SuperLearner package. cate_method method-parameter select CATE estimation method. filter_cate method-parameter estimation filtering. p parameter (generate_cre_dataset function) set number covariates. replace parameter (generate_rules function) allow bootstrapping. cre.print generic function print cre S3 object results. cre.summary generic functions summarize cre S3 object Results. check_input function isolate input checks. estimate_ite_aipw function augmented inverse propensity weighting. plot.cre generic function plot cre S3 object results. test-cre_functional.R test functionality package. stability_selection function causal rules selection.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-1-0","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.1.0 (2022-10-17)","text":"estimate_ite_blp function. take1() function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"CRE 0.1.0 (2022-10-17)","text":"Undesired ‘’ Decision Rule Issue. Causal Rule Selected Issue.","code":""},{"path":[]},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"changed-0-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"CRE 0.0.1 (2021-10-20)","text":"estimate_cate include two methods estimating CATE values. cre added initial checks binary outcome whether include propensity score ITE estimation. estimate_ite_xyz conduct propensity score estimation using helper function.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"added-0-0-1","dir":"Changelog","previous_headings":"","what":"Added","title":"CRE 0.0.1 (2021-10-20)","text":"Example generate_cre_dataset. set_logger get_logger. check_input_data function. generate_cre_dataset function generate synthetic data testing package. test-generate_cre_dataset function test. estimate_ps function estimate propensity score. estimate_ite_xbart function generate ITE estimates using accelerated BART. estimate_ite_xbcf function generate ITE estimates using accelerated BCF. analyze_sensitivity function conduct sensitivity analysis unmeasured confounding. cre function perform entire Causal Rule Ensemble method. estimate_cate function generate CATE estimates ITE estimates select rules. estimate_ite function generate ITE estimates using user-specified method (calls estimate_ite_xyz functions). estimate_ite_bart function generate ITE estimates using BART. estimate_ite_bcf function generate ITE estimates using Bayesian Causal Forests. estimate_ite_cf function generate ITE estimates using Causal Forests. estimate_ite_ipw function generate ITE estimates using IPW. estimate_ite_or function generate ITE estimates using Outcome Regression. estimate_ite_sipw function generate ITE estimates using SIPW. extract_rules function extract list causal rules randomForest GBM models. generate_rules function generate causal rule models using randomForest GBM methods. generate_rules_matrix function convert list causal rules matrix. select_causal_rules function apply penalized regression causal rules. select important ones. split_data function split input data discovery inference subsamples. take1 function create subsample indices.","code":""},{"path":"https://github.com/NSAPH-Software/CRE/news/index.html","id":"removed-0-0-1","dir":"Changelog","previous_headings":"","what":"Removed","title":"CRE 0.0.1 (2021-10-20)","text":"seed argument generate_cre_datase function.","code":""}]
diff --git a/inst/CITATION b/inst/CITATION
new file mode 100644
index 00000000..ade13106
--- /dev/null
+++ b/inst/CITATION
@@ -0,0 +1,25 @@
+bibentry(bibtype = "Article",
+         header = "To cite ‘CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects’ in publications use:",
+         title = "CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects",
+         author = c(person(given = "Riccardo",
+                           family = "Cadei",
+                           role = "aut"),
+                    person(given = "Naeem",
+                           family = "Khoshnevis",
+                           role = "aut"),
+                    person(given = "Kwonsang",
+                           family = "Lee",
+                           role = "aut"),
+                    person(given = "Daniela Maria",
+                           family = "Garcia",
+                           role = "aut"),
+                    person(given = "Falco J. Bargagli",
+                           family = "Stoffi",
+                           role = "aut")),
+         year = "2023",
+         journal = "Journal of Open Source Software",
+         volume = "8",
+         number = "92",
+         pages = "5587",
+         doi = "10.21105/joss.05587",
+         url = "https://doi.org/10.21105/joss.05587")

From 014d82a1dbdbc866763df77ec50a860f0c3a0b22 Mon Sep 17 00:00:00 2001
From: naeemkh <khoshnevis.naeem@gmail.com>
Date: Fri, 15 Dec 2023 14:20:51 -0500
Subject: [PATCH 4/5] fix typo

---
 README.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index cb9f0c48..9fbcc5fd 100644
--- a/README.md
+++ b/README.md
@@ -226,6 +226,16 @@ Please note that the CRE project is released with a [Contributor Code of Conduct
 
 Causal Rule Ensemble ([methodological paper](https://arxiv.org/abs/2009.09036))
 ```bibtex
+@article{bargagli2023causal,
+  title={{Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects}},
+  author={Bargagli-Stoffi, Falco J and Cadei, Riccardo and Lee, Kwonsang and Dominici, Francesca},
+  journal={arXiv preprint arXiv:2009.09036},
+  year={2023}
+}
+```
+
+CRE (software paper)
+```bibtex
 @article{Cadei2023,
   doi = {10.21105/joss.05587},
   url = {https://doi.org/10.21105/joss.05587},
@@ -238,16 +248,6 @@ Causal Rule Ensemble ([methodological paper](https://arxiv.org/abs/2009.09036))
   title = {CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects},
   journal = {Journal of Open Source Software},
 }
-```
-
-CRE (software paper)
-```bibtex
-@article{cadei2023CRE,
-  title = {CRE: an R package for Interpretable Discovery and Estimation of Heterogeneous Treatment Effect},
-  author = {Cadei, Riccardo and Khoshnevis, Naeem and Bargagli-Stoffi, Falco J and Lee, Kwonsang and Garcia, Daniela Maria},
-  year = {2023},
-  journal={Working paper},
-  url = {},
 }
 ```
 

From e3852700484102ceb26bc3cddfcecf372d92bb7a Mon Sep 17 00:00:00 2001
From: Naeem Khoshnevis <khoshnevis.naeem@gmail.com>
Date: Mon, 18 Mar 2024 19:25:25 -0400
Subject: [PATCH 5/5] Update R-CMD-check.yaml

Drop mac os from testing.
---
 .github/workflows/R-CMD-check.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index dd203c8f..42007b13 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -23,7 +23,7 @@ jobs:
       matrix:
         config:
           - {os: windows-latest, r: '4.2'}
-          - {os: macOS-latest, r: '4.2'}
+          # - {os: macOS-latest, r: '4.2'}
           - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
           - {os: ubuntu-20.04,   r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest", http-user-agent: "R/4.1.0 (ubuntu-20.04) R (4.1.0 x86_64-pc-linux-gnu x86_64 linux-gnu) on GitHub Actions" }