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CRE-package.html
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<!DOCTYPE html>
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="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. Data-driven discovery of
heterogeneous treatment effects (HTE) via decision tree methods
has been proposed for this task. Despite its high interpretability,
the single-tree discovery of HTE tends to be highly unstable and to
find an oversimplified representation of treatment heterogeneity.
To accommodate these shortcomings, we propose Causal Rule Ensemble
(CRE), a new method to discover heterogeneous subgroups through an
ensemble-of-trees approach. CRE has the following features:
provides an interpretable representation of the HTE; 2) allows
extensive exploration of complex heterogeneity patterns; and 3)
guarantees high stability in the discovery. The discovered subgroups
are defined in terms of interpretable decision rules, and we develop
a general two-stage approach for subgroup-specific conditional
causal effects estimation, providing theoretical guarantees.
"><title>The CRE package — CRE-package • CRE</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="The CRE package — CRE-package"><meta property="og:description" content="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. Data-driven discovery of
heterogeneous treatment effects (HTE) via decision tree methods
has been proposed for this task. Despite its high interpretability,
the single-tree discovery of HTE tends to be highly unstable and to
find an oversimplified representation of treatment heterogeneity.
To accommodate these shortcomings, we propose Causal Rule Ensemble
(CRE), a new method to discover heterogeneous subgroups through an
ensemble-of-trees approach. CRE has the following features:
provides an interpretable representation of the HTE; 2) allows
extensive exploration of complex heterogeneity patterns; and 3)
guarantees high stability in the discovery. The discovered subgroups
are defined in terms of interpretable decision rules, and we develop
a general two-stage approach for subgroup-specific conditional
causal effects estimation, providing theoretical guarantees.
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<img src="" class="logo" alt=""><h1>The CRE package</h1>
<small class="dont-index">Source: <a href="https://github.com/NSAPH-Software/CRE/blob/HEAD/R/CRE_package.R"><code>R/CRE_package.R</code></a></small>
<div class="d-none name"><code>CRE-package.Rd</code></div>
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<div class="ref-description section level2">
<p>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. Data-driven discovery of
heterogeneous treatment effects (HTE) via decision tree methods
has been proposed for this task. Despite its high interpretability,
the single-tree discovery of HTE tends to be highly unstable and to
find an oversimplified representation of treatment heterogeneity.
To accommodate these shortcomings, we propose Causal Rule Ensemble
(CRE), a new method to discover heterogeneous subgroups through an
ensemble-of-trees approach. CRE has the following features:</p><ol><li><p>provides an interpretable representation of the HTE; 2) allows
extensive exploration of complex heterogeneity patterns; and 3)
guarantees high stability in the discovery. The discovered subgroups
are defined in terms of interpretable decision rules, and we develop
a general two-stage approach for subgroup-specific conditional
causal effects estimation, providing theoretical guarantees.</p></li>
</ol></div>
<div class="section level2">
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
<p>Bargagli-Stoffi, F. J., Cadei, R., Lee, K. and Dominici, F. (2023).
Causal rule ensemble: Interpretable Discovery and Inference of
Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036</p>
</div>
<div class="section level2">
<h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
<p>Naeem Khoshnevis</p>
<p>Daniela Maria Garcia</p>
<p>Riccardo Cadei</p>
<p>Kwonsang Lee</p>
<p>Falco Joannes Bargagli Stoffi</p>
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<p></p><p>Developed by Naeem Khoshnevis, Daniela Maria Garcia, Riccardo Cadei, Kwonsang Lee, Falco Joannes Bargagli Stoffi.</p>
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