From 2c0d551ba20f019970c20e9185e1dcc6433f04c0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Fri, 24 Feb 2023 18:15:28 +0100 Subject: [PATCH 01/57] implemented frequentist S, T and X learners --- causalpy/meta_learners.py | 156 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 156 insertions(+) create mode 100644 causalpy/meta_learners.py diff --git a/causalpy/meta_learners.py b/causalpy/meta_learners.py new file mode 100644 index 00000000..c01d029f --- /dev/null +++ b/causalpy/meta_learners.py @@ -0,0 +1,156 @@ +import pandas as pd +import numpy as np +from sklearn.utils import check_consistent_length +from sklearn.base import clone +#import matplotlib.pyplot as plt +from sklearn.linear_model import LogisticRegression + + +def check_shapes(X, y, treated): + check_consistent_length(X, y, treated) + if len(y.shape) > 1: + raise(ValueError("Target variable shape")) + + +class MetaLearner: + def __init__( + self, + X, y, + treated + ): + check_consistent_length(X, y, treated) + + self.treated = treated + self.X = X + self.y = y + + def predict_cate(self, X): + raise(NotImplementedError()) + + def predict_ate(self, X): + return self.predict_cate(X).mean() + + def summary(self): + raise(NotImplementedError()) + + def plot(self): + raise(NotImplementedError()) + + +class SLearner(MetaLearner): + def __init__(self, + X, + y, + treated, + model): + super().__init__(X=X, y=y, treated=treated) + X_T = pd.concat([X, treated], axis=1) + + self.model = model.fit(X_T, y) + self.cate = self.predict_cate(X) + + def predict_cate(self, X): + X_control = pd.concat([X, 0 * treated], axis=1) + X_treated = pd.concat([X, 0 * treated + 1], axis=1) + return self.model.predict(X_treated) - self.model.predict(X_control) + + +class TLearner(MetaLearner): + def __init__(self, + X, + y, + treated, + model=None, + treated_model=None, + untreated_model=None + ): + super().__init__(X=X, y=y, treated=treated) + + if model is None and (untreated_model is None or treated_model is None): + raise(ValueError("Either model or both of treated_model and untreated_model \ + have to be specified.")) + elif not (model is None or untreated_model is None or treated_model is None): + raise(ValueError("Either model or both of treated_model and untreated_model \ + have to be specified.")) + + if model is not None: + untreated_model = clone(model) + treated_model = clone(model) + + self.untreated_model = untreated_model.fit(X[treated==0], y[treated==0]) + self.treated_model = treated_model.fit(X[treated==1], y[treated==1]) + self.cate = self.predict_cate(X) + + def predict_cate(self, X): + return self.treated_model.predict(X) - self.untreated_model.predict(X) + + +class XLearner(MetaLearner): + def __init__(self, + X, + y, + treated, + model=None, + treated_model=None, + untreated_model=None, + treated_cate_estimator=None, + untreated_cate_estimator=None, + propensity_score_model=None + ): + super().__init__(X=X, y=y, treated=treated) + + if model is None and (untreated_model is None or treated_model is None): + raise(ValueError("""Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.""")) + elif not (model is None or untreated_model is None or treated_model is None): + raise(ValueError("Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.")) + + if propensity_score_model is None: + propensity_score_model = LogisticRegression(penalty='none') + + if model is not None: + treated_model = clone(model) + untreated_model = clone(model) + treated_cate_estimator = clone(model) + untreated_cate_estimator = clone(model) + + # Split data to treated and untreated subsets + X_t, y_t = X[treated==1], y[treated==1] + X_u, y_u = X[treated==0], y[treated==0] + + # Estimate response function + self.models = {'treated': treated_model.fit(X_t, y_t), + 'untreated': untreated_model.fit(X_u, y_u)} + + tau_t = y_t - untreated_model.predict(X_t) + tau_u = treated_model.predict(X_u) - y_u + + # Estimate CATE separately on treated and untreated subsets + self._imputed_treatment_effects = {'treated': tau_t, + 'untreated': tau_u} + + self.models['treated_cate'] = treated_cate_estimator.fit(X_t, tau_t) + self.models['untreated_cate'] = untreated_cate_estimator.fit(X_u, tau_u) + + cate_estimate_treated = treated_cate_estimator.predict(X) + cate_estimate_untreated = treated_cate_estimator.predict(X) + + self.cate_estimates = {'treated': cate_estimate_treated, + 'untreated': cate_estimate_untreated} + + # Find a weight function g + self.models['propensity_score'] = propensity_score_model.fit(X, treated) + g = propensity_score_model.predict(X) + + # Average CATE estimates using g + self.cate = g * cate_estimate_untreated + (1 - g) * cate_estimate_treated + + + def predict_cate(self, X): + cate_estimate_treated = self.models['treated_cate'].predict(X) + cate_estimate_untreated = self.models['untreated_cate'].predict(X) + + g = self.models['propensity_score'].predict(X) + + return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated \ No newline at end of file From ab999b6a63c3bd2943157ee5ee6be02a3f0775df Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 26 Feb 2023 19:33:21 +0100 Subject: [PATCH 02/57] Reformatted. Added bootstrapping. Added DRLearner. --- causalpy/meta_learners.py | 334 +++++++++++++++++++++++++++++++------- 1 file changed, 279 insertions(+), 55 deletions(-) diff --git a/causalpy/meta_learners.py b/causalpy/meta_learners.py index c01d029f..63110927 100644 --- a/causalpy/meta_learners.py +++ b/causalpy/meta_learners.py @@ -4,66 +4,181 @@ from sklearn.base import clone #import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression - - -def check_shapes(X, y, treated): - check_consistent_length(X, y, treated) - if len(y.shape) > 1: - raise(ValueError("Target variable shape")) +from sklearn.base import RegressorMixin +from utils import _is_variable_dummy_coded class MetaLearner: + """ + Base class for meta-learners. + """ def __init__( self, - X, y, - treated - ): + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series + ) -> None: + # Check whether input is appropriate check_consistent_length(X, y, treated) - + if not _is_variable_dummy_coded(treated): + raise(ValueError('Treatment variable is not dummy coded.')) + self.treated = treated self.X = X self.y = y - def predict_cate(self, X): + + def predict_cate(self, X: pd.DataFrame) -> np.array: + """ + Predict conditional average treatment effect for given input X. + """ raise(NotImplementedError()) - def predict_ate(self, X): + + def predict_ate(self, X: pd.DataFrame) -> np.float64: + """ + Predict average treatment effect for given input X. + """ return self.predict_cate(X).mean() + def bootstrap(self, + X_ins: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + X: None, + frac_samples: float = None, + n_samples: int = 1000, + n_iter: int = 1000 + ) -> np.array: + """ + Runs bootstrap n_iter times on a sample of size n_samples. Fits on (X_ins, y, treated), + then predicts on X. + """ + results = [] + for i in range(n_iter): + X_bs = X_ins.sample(frac=frac_samples, + n=n_samples, + replace=True) + y_bs = y.loc[X_bs.index].reset_index(drop=True) + t_bs = treated.loc[X_bs.index].reset_index(drop=True) + + self.fit(X_bs.reset_index(drop=True), y_bs, t_bs) # This overwrites self.models! + results.append(self.predict_cate(X)) + + return np.array(results) + + def ate_confidence_interval(self, + X_ins: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + X: None, + q: float = .95, + frac_samples: float = None, + n_samples: int = 1000, + n_iter: int = 1000): + """ + Estimates confidence intervals for ATE on X using bootstraping. + """ + cates = self.bootstrap(X_ins, + y, + treated, + X, + frac_samples, + n_samples, + n_iter) + return np.quantile(cates, q=q), np.quantile(cates, q=1-q) + + def cate_confidence_interval(self, + X_ins: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + X: None, + q: float = .95, + frac_samples: float = None, + n_samples: int = 1000, + n_iter: int = 1000): + """ + Estimates confidence intervals for CATE on X using bootstraping. + """ + cates = self.bootstrap(X_ins, + y, + treated, + X, + frac_samples, + n_samples, + n_iter) + conf_ints = np.append(np.quantile(cates, .9, axis=0).reshape(-1,1), + np.quantile(cates, .1, axis=0).reshape(-1, 1), + axis=1) + return conf_ints + + + def fit(self, X: pd.DataFrame, + y: pd.Series, + treated: pd.Series): + "Fits model." + raise(NotImplementedError()) + + def summary(self): raise(NotImplementedError()) + def plot(self): raise(NotImplementedError()) class SLearner(MetaLearner): + """ + Implements of S-learner described in [1]. S-learner estimates conditional average treatment effect + with the use of a single model. + + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. + Metalearners for estimating heterogeneous treatment effects using machine learning. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + + """ def __init__(self, - X, - y, - treated, - model): + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model) -> None: super().__init__(X=X, y=y, treated=treated) - X_T = pd.concat([X, treated], axis=1) - - self.model = model.fit(X_T, y) + self.model = model + self.fit(X, y, treated) self.cate = self.predict_cate(X) - def predict_cate(self, X): - X_control = pd.concat([X, 0 * treated], axis=1) - X_treated = pd.concat([X, 0 * treated + 1], axis=1) + def fit(self, X: pd.DataFrame, + y: pd.Series, + treated: pd.Series): + X_T = X.assign(treatment=treated) + self.model = self.model.fit(X_T, y) + return self + + def predict_cate(self, X: pd.DataFrame) -> np.array: + X_control = X.assign(treatment=0) + X_treated = X.assign(treatment=1) return self.model.predict(X_treated) - self.model.predict(X_control) class TLearner(MetaLearner): - def __init__(self, - X, - y, - treated, + """ + Implements of T-learner described in [1]. T-learner fits two separate models to estimate + conditional average treatment effect. + + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. + Metalearners for estimating heterogeneous treatment effects using machine learning. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + + """ + def __init__(self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, model=None, treated_model=None, untreated_model=None - ): + ) -> None: super().__init__(X=X, y=y, treated=treated) if model is None and (untreated_model is None or treated_model is None): @@ -77,15 +192,36 @@ def __init__(self, untreated_model = clone(model) treated_model = clone(model) - self.untreated_model = untreated_model.fit(X[treated==0], y[treated==0]) - self.treated_model = treated_model.fit(X[treated==1], y[treated==1]) + self.models = {'treated': treated_model, + 'untreated': untreated_model} + + self.fit(X, y, treated) self.cate = self.predict_cate(X) - def predict_cate(self, X): - return self.treated_model.predict(X) - self.untreated_model.predict(X) + def fit(self, X: pd.DataFrame, + y: pd.Series, + treated: pd.Series): + self.models['treated'].fit(X[treated==1], y[treated==1]) + self.models['untreated'].fit(X[treated==0], y[treated==0]) + return self + + + def predict_cate(self, X: pd.DataFrame) -> np.array: + treated_model = self.models['treated'] + untreated_model = self.models['untreated'] + return treated_model.predict(X) - untreated_model.predict(X) class XLearner(MetaLearner): + """ + Implements of X-learner introduced in [1]. X-learner estimates conditional average treatment + effect with the use of five separate models. + + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. + Metalearners for estimating heterogeneous treatment effects using machine learning. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + + """ def __init__(self, X, y, @@ -105,52 +241,140 @@ def __init__(self, elif not (model is None or untreated_model is None or treated_model is None): raise(ValueError("Either model or each of treated_model, untreated_model, \ treated_cate_estimator, untreated_cate_estimator has to be specified.")) - + if propensity_score_model is None: - propensity_score_model = LogisticRegression(penalty='none') + propensity_score_model = LogisticRegression(penalty=None) if model is not None: treated_model = clone(model) untreated_model = clone(model) treated_cate_estimator = clone(model) untreated_cate_estimator = clone(model) - + + self.models = {'treated': treated_model, + 'untreated': untreated_model, + 'treated_cate': treated_cate_estimator, + 'untreated_cate': untreated_cate_estimator, + 'propensity': propensity_score_model + } + + self.fit(X, y, treated) + + # Compute cate + cate_t = treated_cate_estimator.predict(X) + cate_u = treated_cate_estimator.predict(X) + g = self.models['propensity'].predict(X) + + self.cate = g * cate_u + (1 - g) * cate_t + + def fit(self, X: pd.DataFrame, + y: pd.Series, + treated: pd.Series): + (treated_model, + untreated_model, + treated_cate_estimator, + untreated_cate_estimator, + propensity_score_model) = self.models.values() + # Split data to treated and untreated subsets X_t, y_t = X[treated==1], y[treated==1] X_u, y_u = X[treated==0], y[treated==0] # Estimate response function - self.models = {'treated': treated_model.fit(X_t, y_t), - 'untreated': untreated_model.fit(X_u, y_u)} + treated_model.fit(X_t, y_t) + untreated_model.fit(X_u, y_u) tau_t = y_t - untreated_model.predict(X_t) tau_u = treated_model.predict(X_u) - y_u # Estimate CATE separately on treated and untreated subsets - self._imputed_treatment_effects = {'treated': tau_t, - 'untreated': tau_u} + treated_cate_estimator.fit(X_t, tau_t) + untreated_cate_estimator.fit(X_u, tau_u) - self.models['treated_cate'] = treated_cate_estimator.fit(X_t, tau_t) - self.models['untreated_cate'] = untreated_cate_estimator.fit(X_u, tau_u) + # Fit propensity score model + propensity_score_model.fit(X, treated) + return self + + + def predict_cate(self, X): + cate_estimate_treated = self.models['treated_cate'].predict(X) + cate_estimate_untreated = self.models['untreated_cate'].predict(X) + + g = self.models['propensity'].predict_proba(X)[:, 1] + + return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated + + +class DRLearner(MetaLearner): + """ + Implements of DR-learner also known as doubly robust learner. DR-learner estimates + conditional average treatment effect with the use of five separate models. + + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. + Metalearners for estimating heterogeneous treatment effects using machine learning. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + + """ + def __init__(self, + X, + y, + treated, + model=None, + treated_model=None, + untreated_model=None, + propensity_score_model=None + ): + super().__init__(X=X, y=y, treated=treated) + + if model is None and (untreated_model is None or treated_model is None): + raise(ValueError("""Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.""")) + elif not (model is None or untreated_model is None or treated_model is None): + raise(ValueError("Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.")) + + if propensity_score_model is None: + propensity_score_model = LogisticRegression(penalty=None) - cate_estimate_treated = treated_cate_estimator.predict(X) - cate_estimate_untreated = treated_cate_estimator.predict(X) + if model is not None: + treated_model = clone(model) + untreated_model = clone(model) - self.cate_estimates = {'treated': cate_estimate_treated, - 'untreated': cate_estimate_untreated} + # Estimate response function + self.models = {'treated': treated_model, + 'untreated': untreated_model, + 'propensity': propensity_score_model} - # Find a weight function g - self.models['propensity_score'] = propensity_score_model.fit(X, treated) - g = propensity_score_model.predict(X) + self.fit(X, y, treated) - # Average CATE estimates using g - self.cate = g * cate_estimate_untreated + (1 - g) * cate_estimate_treated + # Estimate CATE + g = self.models['propensity'].predict_proba(X)[:, 1] + m0 = untreated_model.predict(X) + m1 = treated_model.predict(X) + + self.cate = (treated * (y - m1) / g + m1 + - ((1 - treated) * (y - m0) / (1 - g) + m0)) + + def fit(self, X: pd.DataFrame, + y: pd.Series, + treated: pd.Series): + # Split data to treated and untreated subsets + X_t, y_t = X[treated==1], y[treated==1] + X_u, y_u = X[treated==0], y[treated==0] + + (treated_model, + untreated_model, + propensity_score_model) = self.models.values() + + # Estimate response functions + treated_model.fit(X_t, y_t) + untreated_model.fit(X_u, y_u) + + # Fit propensity score model + propensity_score_model.fit(X, treated) + + return self def predict_cate(self, X): - cate_estimate_treated = self.models['treated_cate'].predict(X) - cate_estimate_untreated = self.models['untreated_cate'].predict(X) - - g = self.models['propensity_score'].predict(X) - - return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated \ No newline at end of file + return self.cate From 9791b9b6a5b253c0d474b747cd1d76da99f41b98 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 26 Feb 2023 19:48:20 +0100 Subject: [PATCH 03/57] Fixed doc-string for DRLearner --- causalpy/meta_learners.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/causalpy/meta_learners.py b/causalpy/meta_learners.py index 63110927..9042f617 100644 --- a/causalpy/meta_learners.py +++ b/causalpy/meta_learners.py @@ -308,11 +308,7 @@ def predict_cate(self, X): class DRLearner(MetaLearner): """ Implements of DR-learner also known as doubly robust learner. DR-learner estimates - conditional average treatment effect with the use of five separate models. - - [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. - Metalearners for estimating heterogeneous treatment effects using machine learning. - Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + conditional average treatment effect with the use of three separate models. """ def __init__(self, From 100f8d79e14f7ce8acb9f221a1e23e59a61966c0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 26 Feb 2023 20:04:12 +0100 Subject: [PATCH 04/57] renamed meta_learners.py to skl_meta_learners.py --- causalpy/{meta_learners.py => skl_meta_learners.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename causalpy/{meta_learners.py => skl_meta_learners.py} (100%) diff --git a/causalpy/meta_learners.py b/causalpy/skl_meta_learners.py similarity index 100% rename from causalpy/meta_learners.py rename to causalpy/skl_meta_learners.py From 5874281a351b836651d371f14693935e4061b6d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 26 Feb 2023 20:13:36 +0100 Subject: [PATCH 05/57] imported skl_meta_learners --- causalpy/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/causalpy/__init__.py b/causalpy/__init__.py index 135ae8e8..8d788910 100644 --- a/causalpy/__init__.py +++ b/causalpy/__init__.py @@ -5,6 +5,7 @@ import causalpy.skl_experiments import causalpy.skl_models from causalpy.version import __version__ +import causalpy.skl_meta_learners from .data import load_data From df90a5292dcc7374a6ebb819ec3bd2937935c70d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 27 Feb 2023 10:12:03 +0100 Subject: [PATCH 06/57] minor code style fixes --- causalpy/skl_meta_learners.py | 174 +++++++++++++++++----------------- 1 file changed, 86 insertions(+), 88 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 9042f617..9e4e5d1f 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -2,16 +2,15 @@ import numpy as np from sklearn.utils import check_consistent_length from sklearn.base import clone -#import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression -from sklearn.base import RegressorMixin -from utils import _is_variable_dummy_coded +from causalpy.utils import _is_variable_dummy_coded class MetaLearner: """ Base class for meta-learners. """ + def __init__( self, X: pd.DataFrame, @@ -21,26 +20,24 @@ def __init__( # Check whether input is appropriate check_consistent_length(X, y, treated) if not _is_variable_dummy_coded(treated): - raise(ValueError('Treatment variable is not dummy coded.')) + raise ValueError('Treatment variable is not dummy coded.') self.treated = treated self.X = X self.y = y - def predict_cate(self, X: pd.DataFrame) -> np.array: """ Predict conditional average treatment effect for given input X. """ - raise(NotImplementedError()) - + raise NotImplementedError() def predict_ate(self, X: pd.DataFrame) -> np.float64: """ Predict average treatment effect for given input X. """ return self.predict_cate(X).mean() - + def bootstrap(self, X_ins: pd.DataFrame, y: pd.Series, @@ -49,22 +46,23 @@ def bootstrap(self, frac_samples: float = None, n_samples: int = 1000, n_iter: int = 1000 - ) -> np.array: + ) -> np.array: """ - Runs bootstrap n_iter times on a sample of size n_samples. Fits on (X_ins, y, treated), - then predicts on X. + Runs bootstrap n_iter times on a sample of size n_samples. + Fits on (X_ins, y, treated), then predicts on X. """ results = [] - for i in range(n_iter): + for _ in range(n_iter): X_bs = X_ins.sample(frac=frac_samples, n=n_samples, replace=True) y_bs = y.loc[X_bs.index].reset_index(drop=True) t_bs = treated.loc[X_bs.index].reset_index(drop=True) - self.fit(X_bs.reset_index(drop=True), y_bs, t_bs) # This overwrites self.models! + # This overwrites self.models! + self.fit(X_bs.reset_index(drop=True), y_bs, t_bs) results.append(self.predict_cate(X)) - + return np.array(results) def ate_confidence_interval(self, @@ -107,38 +105,39 @@ def cate_confidence_interval(self, frac_samples, n_samples, n_iter) - conf_ints = np.append(np.quantile(cates, .9, axis=0).reshape(-1,1), - np.quantile(cates, .1, axis=0).reshape(-1, 1), + conf_ints = np.append(np.quantile(cates, q, axis=0).reshape(-1, 1), + np.quantile(cates, 1 - q, axis=0).reshape(-1, 1), axis=1) return conf_ints - - def fit(self, X: pd.DataFrame, + def fit(self, + X: pd.DataFrame, y: pd.Series, treated: pd.Series): "Fits model." - raise(NotImplementedError()) - + raise NotImplementedError() def summary(self): - raise(NotImplementedError()) - + "Prints summary. Conent is undecided yet." + raise NotImplementedError() def plot(self): - raise(NotImplementedError()) - + "Plots results. Content is undecided yet." + raise NotImplementedError() + class SLearner(MetaLearner): """ - Implements of S-learner described in [1]. S-learner estimates conditional average treatment effect - with the use of a single model. - + Implements of S-learner described in [1]. S-learner estimates conditional average + treatment effect with the use of a single model. + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. """ - def __init__(self, + + def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, @@ -147,14 +146,14 @@ def __init__(self, self.model = model self.fit(X, y, treated) self.cate = self.predict_cate(X) - + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): X_T = X.assign(treatment=treated) self.model = self.model.fit(X_T, y) return self - + def predict_cate(self, X: pd.DataFrame) -> np.array: X_control = X.assign(treatment=0) X_treated = X.assign(treatment=1) @@ -165,12 +164,13 @@ class TLearner(MetaLearner): """ Implements of T-learner described in [1]. T-learner fits two separate models to estimate conditional average treatment effect. - + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. """ + def __init__(self, X: pd.DataFrame, y: pd.Series, @@ -178,51 +178,51 @@ def __init__(self, model=None, treated_model=None, untreated_model=None - ) -> None: + ) -> None: super().__init__(X=X, y=y, treated=treated) - + if model is None and (untreated_model is None or treated_model is None): - raise(ValueError("Either model or both of treated_model and untreated_model \ + raise (ValueError("Either model or both of treated_model and untreated_model \ have to be specified.")) elif not (model is None or untreated_model is None or treated_model is None): - raise(ValueError("Either model or both of treated_model and untreated_model \ + raise (ValueError("Either model or both of treated_model and untreated_model \ have to be specified.")) - + if model is not None: untreated_model = clone(model) treated_model = clone(model) - + self.models = {'treated': treated_model, 'untreated': untreated_model} - + self.fit(X, y, treated) self.cate = self.predict_cate(X) - + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): - self.models['treated'].fit(X[treated==1], y[treated==1]) - self.models['untreated'].fit(X[treated==0], y[treated==0]) + self.models['treated'].fit(X[treated == 1], y[treated == 1]) + self.models['untreated'].fit(X[treated == 0], y[treated == 0]) return self - def predict_cate(self, X: pd.DataFrame) -> np.array: treated_model = self.models['treated'] untreated_model = self.models['untreated'] return treated_model.predict(X) - untreated_model.predict(X) - + class XLearner(MetaLearner): """ Implements of X-learner introduced in [1]. X-learner estimates conditional average treatment effect with the use of five separate models. - + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. """ - def __init__(self, + + def __init__(self, X, y, treated, @@ -232,41 +232,41 @@ def __init__(self, treated_cate_estimator=None, untreated_cate_estimator=None, propensity_score_model=None - ): + ): super().__init__(X=X, y=y, treated=treated) - + if model is None and (untreated_model is None or treated_model is None): - raise(ValueError("""Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.""")) + raise ValueError("""Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.""") elif not (model is None or untreated_model is None or treated_model is None): - raise(ValueError("Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.")) - + raise ValueError("Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.") + if propensity_score_model is None: propensity_score_model = LogisticRegression(penalty=None) - + if model is not None: treated_model = clone(model) untreated_model = clone(model) treated_cate_estimator = clone(model) untreated_cate_estimator = clone(model) - + self.models = {'treated': treated_model, 'untreated': untreated_model, 'treated_cate': treated_cate_estimator, 'untreated_cate': untreated_cate_estimator, 'propensity': propensity_score_model - } + } self.fit(X, y, treated) - + # Compute cate cate_t = treated_cate_estimator.predict(X) cate_u = treated_cate_estimator.predict(X) g = self.models['propensity'].predict(X) - + self.cate = g * cate_u + (1 - g) * cate_t - + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): @@ -277,41 +277,40 @@ def fit(self, X: pd.DataFrame, propensity_score_model) = self.models.values() # Split data to treated and untreated subsets - X_t, y_t = X[treated==1], y[treated==1] - X_u, y_u = X[treated==0], y[treated==0] - + X_t, y_t = X[treated == 1], y[treated == 1] + X_u, y_u = X[treated == 0], y[treated == 0] + # Estimate response function treated_model.fit(X_t, y_t) untreated_model.fit(X_u, y_u) - + tau_t = y_t - untreated_model.predict(X_t) tau_u = treated_model.predict(X_u) - y_u - + # Estimate CATE separately on treated and untreated subsets treated_cate_estimator.fit(X_t, tau_t) untreated_cate_estimator.fit(X_u, tau_u) - + # Fit propensity score model propensity_score_model.fit(X, treated) return self - def predict_cate(self, X): cate_estimate_treated = self.models['treated_cate'].predict(X) cate_estimate_untreated = self.models['untreated_cate'].predict(X) - + g = self.models['propensity'].predict_proba(X)[:, 1] - + return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated class DRLearner(MetaLearner): """ - Implements of DR-learner also known as doubly robust learner. DR-learner estimates - conditional average treatment effect with the use of three separate models. + Implements of DR-learner also known as doubly robust learner as described in . """ - def __init__(self, + + def __init__(self, X, y, treated, @@ -319,58 +318,57 @@ def __init__(self, treated_model=None, untreated_model=None, propensity_score_model=None - ): + ): super().__init__(X=X, y=y, treated=treated) - + if model is None and (untreated_model is None or treated_model is None): - raise(ValueError("""Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.""")) + raise ValueError("""Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.""") elif not (model is None or untreated_model is None or treated_model is None): - raise(ValueError("Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.")) - + raise ValueError("Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified.") + if propensity_score_model is None: propensity_score_model = LogisticRegression(penalty=None) - + if model is not None: treated_model = clone(model) untreated_model = clone(model) - + # Estimate response function self.models = {'treated': treated_model, 'untreated': untreated_model, 'propensity': propensity_score_model} - + self.fit(X, y, treated) - + # Estimate CATE g = self.models['propensity'].predict_proba(X)[:, 1] m0 = untreated_model.predict(X) m1 = treated_model.predict(X) self.cate = (treated * (y - m1) / g + m1 - - ((1 - treated) * (y - m0) / (1 - g) + m0)) - + - ((1 - treated) * (y - m0) / (1 - g) + m0)) def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): # Split data to treated and untreated subsets - X_t, y_t = X[treated==1], y[treated==1] - X_u, y_u = X[treated==0], y[treated==0] + X_t, y_t = X[treated == 1], y[treated == 1] + X_u, y_u = X[treated == 0], y[treated == 0] (treated_model, untreated_model, propensity_score_model) = self.models.values() - # Estimate response functions + # Estimate response functions treated_model.fit(X_t, y_t) untreated_model.fit(X_u, y_u) # Fit propensity score model propensity_score_model.fit(X, treated) - + return self - + def predict_cate(self, X): return self.cate From b8a3dff265b7698e01944b50d21eb7841bf00fb7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 27 Feb 2023 13:56:01 +0100 Subject: [PATCH 07/57] mostly stylistic changes --- causalpy/skl_meta_learners.py | 334 +++++++++++++++++----------------- 1 file changed, 164 insertions(+), 170 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 9e4e5d1f..26f90769 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -1,61 +1,52 @@ -import pandas as pd +import matplotlib.pyplot as plt import numpy as np -from sklearn.utils import check_consistent_length +import pandas as pd from sklearn.base import clone from sklearn.linear_model import LogisticRegression +from sklearn.utils import check_consistent_length + from causalpy.utils import _is_variable_dummy_coded class MetaLearner: - """ - Base class for meta-learners. - """ + "Base class for meta-learners." - def __init__( - self, - X: pd.DataFrame, - y: pd.Series, - treated: pd.Series - ) -> None: + def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: # Check whether input is appropriate check_consistent_length(X, y, treated) if not _is_variable_dummy_coded(treated): raise ValueError('Treatment variable is not dummy coded.') + self.cate = None self.treated = treated self.X = X self.y = y def predict_cate(self, X: pd.DataFrame) -> np.array: - """ - Predict conditional average treatment effect for given input X. - """ + "Predict conditional average treatment effect for given input X." raise NotImplementedError() def predict_ate(self, X: pd.DataFrame) -> np.float64: - """ - Predict average treatment effect for given input X. - """ + "Predict average treatment effect for given input X." return self.predict_cate(X).mean() - def bootstrap(self, - X_ins: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - X: None, - frac_samples: float = None, - n_samples: int = 1000, - n_iter: int = 1000 - ) -> np.array: + def bootstrap( + self, + X_ins: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + X: None, + frac_samples: float = None, + n_samples: int = 1000, + n_iter: int = 1000 + ) -> np.array: """ Runs bootstrap n_iter times on a sample of size n_samples. Fits on (X_ins, y, treated), then predicts on X. """ results = [] for _ in range(n_iter): - X_bs = X_ins.sample(frac=frac_samples, - n=n_samples, - replace=True) + X_bs = X_ins.sample(frac=frac_samples, n=n_samples, replace=True) y_bs = y.loc[X_bs.index].reset_index(drop=True) t_bs = treated.loc[X_bs.index].reset_index(drop=True) @@ -65,83 +56,73 @@ def bootstrap(self, return np.array(results) - def ate_confidence_interval(self, - X_ins: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - X: None, - q: float = .95, - frac_samples: float = None, - n_samples: int = 1000, - n_iter: int = 1000): - """ - Estimates confidence intervals for ATE on X using bootstraping. - """ - cates = self.bootstrap(X_ins, - y, - treated, - X, - frac_samples, - n_samples, - n_iter) - return np.quantile(cates, q=q), np.quantile(cates, q=1-q) - - def cate_confidence_interval(self, - X_ins: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - X: None, - q: float = .95, - frac_samples: float = None, - n_samples: int = 1000, - n_iter: int = 1000): - """ - Estimates confidence intervals for CATE on X using bootstraping. - """ - cates = self.bootstrap(X_ins, - y, - treated, - X, - frac_samples, - n_samples, - n_iter) - conf_ints = np.append(np.quantile(cates, q, axis=0).reshape(-1, 1), - np.quantile(cates, 1 - q, axis=0).reshape(-1, 1), - axis=1) + def ate_confidence_interval( + self, + X_ins: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + X: None, + q: float = .95, + frac_samples: float = None, + n_samples: int = 1000, + n_iter: int = 1000 + ): + "Estimates confidence intervals for ATE on X using bootstraping." + cates = self.bootstrap(X_ins, y, treated, X, frac_samples, n_samples, n_iter) + return np.quantile(cates, q=q), np.quantile(cates, q=1 - q) + + + def cate_confidence_interval( + self, + X_ins: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + X: None, + q: float = .95, + frac_samples: float = None, + n_samples: int = 1000, + n_iter: int = 1000 + ): + "Estimates confidence intervals for CATE on X using bootstraping." + cates = self.bootstrap(X_ins, y, treated, X, frac_samples, n_samples, n_iter) + conf_ints = np.append( + np.quantile(cates, q, axis=0).reshape(-1, 1), + np.quantile(cates, 1 - q, axis=0).reshape(-1, 1), + axis=1, + ) return conf_ints - def fit(self, - X: pd.DataFrame, - y: pd.Series, - treated: pd.Series): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): "Fits model." raise NotImplementedError() def summary(self): - "Prints summary. Conent is undecided yet." - raise NotImplementedError() + "Prints summary." + conf_ints = self.ate_confidence_interval(self.X, self.y, self.treated, self.X) + print(f"Number of observations: {self.X.shape[0]}") + print(f"Number of treated observations: {self.treated.sum()}") + print(f"Average treatement effect (ATE): {self.predict_ate(self.X.shape[0])}") + print(f"Confidence interval for ATE: {conf_ints}") def plot(self): "Plots results. Content is undecided yet." - raise NotImplementedError() + plt.hist(self.cate, bins=40, density=True) class SLearner(MetaLearner): """ Implements of S-learner described in [1]. S-learner estimates conditional average - treatment effect with the use of a single model. + treatment effect with the use of a single model. - [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. """ - def __init__(self, - X: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - model) -> None: + def __init__( + self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, model + ) -> None: super().__init__(X=X, y=y, treated=treated) self.model = model self.fit(X, y, treated) @@ -150,8 +131,8 @@ def __init__(self, def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): - X_T = X.assign(treatment=treated) - self.model = self.model.fit(X_T, y) + X_t = X.assign(treatment=treated) + self.model = self.model.fit(X_t, y) return self def predict_cate(self, X: pd.DataFrame) -> np.array: @@ -163,84 +144,91 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: class TLearner(MetaLearner): """ Implements of T-learner described in [1]. T-learner fits two separate models to estimate - conditional average treatment effect. + conditional average treatment effect. - [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. """ - def __init__(self, - X: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - model=None, - treated_model=None, - untreated_model=None - ) -> None: + def __init__( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model=None, + treated_model=None, + untreated_model=None + ) -> None: super().__init__(X=X, y=y, treated=treated) if model is None and (untreated_model is None or treated_model is None): - raise (ValueError("Either model or both of treated_model and untreated_model \ - have to be specified.")) + raise ValueError( + "Either model or both of treated_model and untreated_model \ + have to be specified." + ) elif not (model is None or untreated_model is None or treated_model is None): - raise (ValueError("Either model or both of treated_model and untreated_model \ - have to be specified.")) + raise ValueError( + "Either model or both of treated_model and untreated_model \ + have to be specified." + ) if model is not None: untreated_model = clone(model) treated_model = clone(model) - self.models = {'treated': treated_model, - 'untreated': untreated_model} + self.models = {"treated": treated_model, "untreated": untreated_model} self.fit(X, y, treated) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, - y: pd.Series, - treated: pd.Series): - self.models['treated'].fit(X[treated == 1], y[treated == 1]) - self.models['untreated'].fit(X[treated == 0], y[treated == 0]) + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): + self.models["treated"].fit(X[treated == 1], y[treated == 1]) + self.models["untreated"].fit(X[treated == 0], y[treated == 0]) return self def predict_cate(self, X: pd.DataFrame) -> np.array: - treated_model = self.models['treated'] - untreated_model = self.models['untreated'] + treated_model = self.models["treated"] + untreated_model = self.models["untreated"] return treated_model.predict(X) - untreated_model.predict(X) class XLearner(MetaLearner): """ Implements of X-learner introduced in [1]. X-learner estimates conditional average treatment - effect with the use of five separate models. + effect with the use of five separate models. - [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. """ - def __init__(self, - X, - y, - treated, - model=None, - treated_model=None, - untreated_model=None, - treated_cate_estimator=None, - untreated_cate_estimator=None, - propensity_score_model=None - ): + def __init__( + self, + X, + y, + treated, + model=None, + treated_model=None, + untreated_model=None, + treated_cate_estimator=None, + untreated_cate_estimator=None, + propensity_score_model=None + ): super().__init__(X=X, y=y, treated=treated) if model is None and (untreated_model is None or treated_model is None): - raise ValueError("""Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.""") + raise ValueError( + "Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified." + ) elif not (model is None or untreated_model is None or treated_model is None): - raise ValueError("Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.") + raise ValueError( + "Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified." + ) if propensity_score_model is None: propensity_score_model = LogisticRegression(penalty=None) @@ -251,30 +239,31 @@ def __init__(self, treated_cate_estimator = clone(model) untreated_cate_estimator = clone(model) - self.models = {'treated': treated_model, - 'untreated': untreated_model, - 'treated_cate': treated_cate_estimator, - 'untreated_cate': untreated_cate_estimator, - 'propensity': propensity_score_model - } + self.models = { + "treated": treated_model, + "untreated": untreated_model, + "treated_cate": treated_cate_estimator, + "untreated_cate": untreated_cate_estimator, + "propensity": propensity_score_model + } self.fit(X, y, treated) # Compute cate cate_t = treated_cate_estimator.predict(X) cate_u = treated_cate_estimator.predict(X) - g = self.models['propensity'].predict(X) + g = self.models["propensity"].predict(X) self.cate = g * cate_u + (1 - g) * cate_t - def fit(self, X: pd.DataFrame, - y: pd.Series, - treated: pd.Series): - (treated_model, - untreated_model, - treated_cate_estimator, - untreated_cate_estimator, - propensity_score_model) = self.models.values() + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): + ( + treated_model, + untreated_model, + treated_cate_estimator, + untreated_cate_estimator, + propensity_score_model + ) = self.models.values() # Split data to treated and untreated subsets X_t, y_t = X[treated == 1], y[treated == 1] @@ -296,10 +285,10 @@ def fit(self, X: pd.DataFrame, return self def predict_cate(self, X): - cate_estimate_treated = self.models['treated_cate'].predict(X) - cate_estimate_untreated = self.models['untreated_cate'].predict(X) + cate_estimate_treated = self.models["treated_cate"].predict(X) + cate_estimate_untreated = self.models["untreated_cate"].predict(X) - g = self.models['propensity'].predict_proba(X)[:, 1] + g = self.models["propensity"].predict_proba(X)[:, 1] return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated @@ -310,23 +299,28 @@ class DRLearner(MetaLearner): """ - def __init__(self, - X, - y, - treated, - model=None, - treated_model=None, - untreated_model=None, - propensity_score_model=None - ): + def __init__( + self, + X, + y, + treated, + model=None, + treated_model=None, + untreated_model=None, + propensity_score_model=None + ): super().__init__(X=X, y=y, treated=treated) if model is None and (untreated_model is None or treated_model is None): - raise ValueError("""Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.""") + raise ValueError( + "Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified." + ) elif not (model is None or untreated_model is None or treated_model is None): - raise ValueError("Either model or each of treated_model, untreated_model, \ - treated_cate_estimator, untreated_cate_estimator has to be specified.") + raise ValueError( + "Either model or each of treated_model, untreated_model, \ + treated_cate_estimator, untreated_cate_estimator has to be specified." + ) if propensity_score_model is None: propensity_score_model = LogisticRegression(penalty=None) @@ -336,30 +330,28 @@ def __init__(self, untreated_model = clone(model) # Estimate response function - self.models = {'treated': treated_model, - 'untreated': untreated_model, - 'propensity': propensity_score_model} + self.models = { + "treated": treated_model, + "untreated": untreated_model, + "propensity": propensity_score_model + } self.fit(X, y, treated) # Estimate CATE - g = self.models['propensity'].predict_proba(X)[:, 1] + g = self.models["propensity"].predict_proba(X)[:, 1] m0 = untreated_model.predict(X) m1 = treated_model.predict(X) self.cate = (treated * (y - m1) / g + m1 - ((1 - treated) * (y - m0) / (1 - g) + m0)) - def fit(self, X: pd.DataFrame, - y: pd.Series, - treated: pd.Series): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): # Split data to treated and untreated subsets X_t, y_t = X[treated == 1], y[treated == 1] X_u, y_u = X[treated == 0], y[treated == 0] - (treated_model, - untreated_model, - propensity_score_model) = self.models.values() + treated_model, untreated_model, propensity_score_model = self.models.values() # Estimate response functions treated_model.fit(X_t, y_t) @@ -371,4 +363,6 @@ def fit(self, X: pd.DataFrame, return self def predict_cate(self, X): - return self.cate + m1 = self.models["treated"].predict(X) + m0 = self.models["untreated"].predict(X) + return m1 - m0 \ No newline at end of file From 020a65fda121ff37301af93c36ce9a2cd5d6871c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 27 Feb 2023 14:00:25 +0100 Subject: [PATCH 08/57] fixed an import --- causalpy/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/causalpy/__init__.py b/causalpy/__init__.py index 8d788910..b92def19 100644 --- a/causalpy/__init__.py +++ b/causalpy/__init__.py @@ -3,9 +3,9 @@ import causalpy.pymc_experiments import causalpy.pymc_models import causalpy.skl_experiments +import causalpy.skl_meta_learners import causalpy.skl_models from causalpy.version import __version__ -import causalpy.skl_meta_learners from .data import load_data From 667d3b4d2b96d542e82dbb3d3e0dd36af1ea92f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Tue, 28 Feb 2023 22:45:54 +0100 Subject: [PATCH 09/57] bootstraping does not overwrite self.models anymore --- causalpy/skl_meta_learners.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 26f90769..62462a7a 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -21,6 +21,7 @@ def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: self.treated = treated self.X = X self.y = y + self.models = {} def predict_cate(self, X: pd.DataFrame) -> np.array: "Predict conditional average treatment effect for given input X." @@ -44,16 +45,19 @@ def bootstrap( Runs bootstrap n_iter times on a sample of size n_samples. Fits on (X_ins, y, treated), then predicts on X. """ + # Bootstraping overwrites these attributes + models, cate = self.models, self.cate results = [] for _ in range(n_iter): X_bs = X_ins.sample(frac=frac_samples, n=n_samples, replace=True) y_bs = y.loc[X_bs.index].reset_index(drop=True) t_bs = treated.loc[X_bs.index].reset_index(drop=True) - # This overwrites self.models! self.fit(X_bs.reset_index(drop=True), y_bs, t_bs) results.append(self.predict_cate(X)) + self.models = models + self.cate = cate return np.array(results) def ate_confidence_interval( @@ -124,21 +128,20 @@ def __init__( self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, model ) -> None: super().__init__(X=X, y=y, treated=treated) - self.model = model + self.models['model'] = model self.fit(X, y, treated) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, - y: pd.Series, - treated: pd.Series): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): X_t = X.assign(treatment=treated) - self.model = self.model.fit(X_t, y) + self.models['model'].fit(X_t, y) return self def predict_cate(self, X: pd.DataFrame) -> np.array: X_control = X.assign(treatment=0) X_treated = X.assign(treatment=1) - return self.model.predict(X_treated) - self.model.predict(X_control) + m = self.models['model'] + return m.predict(X_treated) - m.predict(X_control) class TLearner(MetaLearner): From d05c1569e9625b99add1aa2acbe5b8a409504d9f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 1 Mar 2023 10:41:52 +0100 Subject: [PATCH 10/57] fixed a citation in docstring --- causalpy/skl_meta_learners.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 62462a7a..f5112b35 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -298,7 +298,11 @@ def predict_cate(self, X): class DRLearner(MetaLearner): """ - Implements of DR-learner also known as doubly robust learner as described in . + Implements of DR-learner also known as doubly robust learner as described in [1]. + + [1] Curth, Alicia, Mihaela van der Schaar. + Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms. + International Conference on Artificial Intelligence and Statistics, pp. 1810-1818 (2021). """ From 542e129f03817776daedf766f444b3707a242cf3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 1 Mar 2023 16:56:30 +0100 Subject: [PATCH 11/57] added _fit function to reduce boilerplate code --- causalpy/utils.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/causalpy/utils.py b/causalpy/utils.py index 50e79694..bbc02fdd 100644 --- a/causalpy/utils.py +++ b/causalpy/utils.py @@ -1,5 +1,16 @@ import pandas as pd +from causalpy.pymc_models import ModelBuilder + + +def _fit(model, X, y, coords): + """Fits model to X, y, where model is either a sklearn model or a ModelBuilder + instance. In the later case it passes coords, in the first case coords is ignored.""" + if isinstance(model, ModelBuilder): + model.fit(X, y, coords) + else: + model.fit(X, y) + def _is_variable_dummy_coded(series: pd.Series) -> bool: """Check if a data in the provided Series is dummy coded. It should be 0 or 1 From 5f8a62f52ae65e4fa91178b89e9a8d296320e086 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 1 Mar 2023 17:03:20 +0100 Subject: [PATCH 12/57] refactored --- causalpy/skl_meta_learners.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index f5112b35..2a07fca4 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -5,7 +5,7 @@ from sklearn.linear_model import LogisticRegression from sklearn.utils import check_consistent_length -from causalpy.utils import _is_variable_dummy_coded +from causalpy.utils import _is_variable_dummy_coded, _fit class MetaLearner: @@ -96,7 +96,7 @@ def cate_confidence_interval( ) return conf_ints - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): "Fits model." raise NotImplementedError() @@ -129,12 +129,15 @@ def __init__( ) -> None: super().__init__(X=X, y=y, treated=treated) self.models['model'] = model - self.fit(X, y, treated) + + COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + + self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): X_t = X.assign(treatment=treated) - self.models['model'].fit(X_t, y) + _fit(model=self.models['model'], X=X_t, y=y, coords=coords) return self def predict_cate(self, X: pd.DataFrame) -> np.array: @@ -186,9 +189,11 @@ def __init__( self.fit(X, y, treated) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): - self.models["treated"].fit(X[treated == 1], y[treated == 1]) - self.models["untreated"].fit(X[treated == 0], y[treated == 0]) + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + X_t, y_t = X[treated == 1], y[treated == 1] + X_u, y_u = X[treated == 0], y[treated == 0] + _fit(model=self.models["treated"], X=X_t, y=y_t, coords=coords) + _fit(model=self.models["untreated"], X=X_u, y=y_u, coords=coords) return self def predict_cate(self, X: pd.DataFrame) -> np.array: @@ -259,7 +264,7 @@ def __init__( self.cate = g * cate_u + (1 - g) * cate_t - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): ( treated_model, untreated_model, @@ -353,7 +358,7 @@ def __init__( self.cate = (treated * (y - m1) / g + m1 - ((1 - treated) * (y - m0) / (1 - g) + m0)) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): # Split data to treated and untreated subsets X_t, y_t = X[treated == 1], y[treated == 1] X_u, y_u = X[treated == 0], y[treated == 0] From 759b9e256f7a52a907f29983db60fc7df3fbc159 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 1 Mar 2023 17:06:31 +0100 Subject: [PATCH 13/57] added BARTModel --- causalpy/pymc_models.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 7b6efbf0..d9efc51a 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -5,6 +5,8 @@ import pandas as pd import pymc as pm from arviz import r2_score +import pymc_bart as pmb + class ModelBuilder(pm.Model): @@ -113,3 +115,19 @@ def build_model(self, X, y, coords): sigma = pm.HalfNormal("sigma", 1) mu = pm.Deterministic("mu", pm.math.dot(X, beta), dims="obs_ind") pm.Normal("y_hat", mu, sigma, observed=y, dims="obs_ind") + + +class BARTModel(ModelBuilder): + "Class for building BART based models for meta-learners." + + def __init__(self, sample_kwargs=None, m=20, sigma=1): + self.m = m + self.sigma = sigma + super().__init__(sample_kwargs) + + def build_model(self, X, y, coords=None): + with self: + self.add_coords(coords) + X = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) + mu = pmb.BART("mu", X, y, m=self.m, dims="obs_ind") + pm.Normal("y_hat", mu=mu, sigma=self.sigma, observed=y, dims="obs_ind") \ No newline at end of file From 8c03319dc486a86fd818790609bc6ecbd231713b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 1 Mar 2023 17:09:49 +0100 Subject: [PATCH 14/57] outlined pymc meta-learners --- causalpy/pymc_meta_learners.py | 60 ++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 causalpy/pymc_meta_learners.py diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py new file mode 100644 index 00000000..66464359 --- /dev/null +++ b/causalpy/pymc_meta_learners.py @@ -0,0 +1,60 @@ +import pandas as pd +import numpy as np +import pymc as pm + + +from causalpy.skl_meta_learners import SLearner, TLearner, XLearner, DRLearner + + +class BayesianMetaLearner: + "Base class for PyMC based meta-learners." + def plot(self): + # TODO + pass + + def summary(self): + # TODO + pass + + +class BayesianSLearner(BayesianMetaLearner, SLearner): + "PyMC version of S-Learner." + + def predict_cate(self, X: pd.DataFrame) -> np.array: + X_untreated = X.assign(treatment=0) + X_treated = X.assign(treatment=1) + m = self.models['model'] + + pred_treated = m.predict(X_treated)["posterior_predictive"].mu + pred_untreated = m.predict(X_untreated)["posterior_predictive"].mu + + return pred_treated - pred_untreated + + +class BayesianTLearner(BayesianMetaLearner, TLearner): + "PyMC version of T-Learner." + + def predict_cate(self, X: pd.DataFrame) -> np.array: + treated_model = self.models["treated"] + untreated_model = self.models["untreated"] + + pred_treated = treated_model.predict(X)["posterior_predictive"].mu + pred_untreated = untreated_model.predict(X)["posterior_predictive"].mu + + return pred_treated - pred_untreated + + +class BayesianXLearner(BayesianMetaLearner, XLearner): + "PyMC version of X-Learner." + + def predict_cate(self, X: pd.DataFrame) -> np.array: + # TODO + pass + + +class BayesianDRLearner(BayesianMetaLearner, DRLearner): + "PyMC version of DR-Learner." + + def predict_cate(self, X: pd.DataFrame) -> np.array: + # TODO + pass \ No newline at end of file From 18baff5eaca3848ef8c966c47f77bea10bb8e71f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 2 Mar 2023 14:06:53 +0100 Subject: [PATCH 15/57] minor changes helping pymc integration --- causalpy/skl_meta_learners.py | 43 +++++++++++++++++++---------------- 1 file changed, 24 insertions(+), 19 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 2a07fca4..2bfcc4d6 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -1,7 +1,7 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd -from sklearn.base import clone +from copy import deepcopy from sklearn.linear_model import LogisticRegression from sklearn.utils import check_consistent_length @@ -181,12 +181,14 @@ def __init__( ) if model is not None: - untreated_model = clone(model) - treated_model = clone(model) + untreated_model = deepcopy(model) + treated_model = deepcopy(model) self.models = {"treated": treated_model, "untreated": untreated_model} - self.fit(X, y, treated) + COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + + self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): @@ -242,10 +244,10 @@ def __init__( propensity_score_model = LogisticRegression(penalty=None) if model is not None: - treated_model = clone(model) - untreated_model = clone(model) - treated_cate_estimator = clone(model) - untreated_cate_estimator = clone(model) + treated_model = deepcopy(model) + untreated_model = deepcopy(model) + treated_cate_estimator = deepcopy(model) + untreated_cate_estimator = deepcopy(model) self.models = { "treated": treated_model, @@ -255,7 +257,9 @@ def __init__( "propensity": propensity_score_model } - self.fit(X, y, treated) + COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + + self.fit(X, y, treated, coords=COORDS) # Compute cate cate_t = treated_cate_estimator.predict(X) @@ -278,8 +282,8 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): X_u, y_u = X[treated == 0], y[treated == 0] # Estimate response function - treated_model.fit(X_t, y_t) - untreated_model.fit(X_u, y_u) + _fit(treated_model, X_t, y_t, coords) + _fit(untreated_model, X_u, y_u, coords) tau_t = y_t - untreated_model.predict(X_t) tau_u = treated_model.predict(X_u) - y_u @@ -289,7 +293,7 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): untreated_cate_estimator.fit(X_u, tau_u) # Fit propensity score model - propensity_score_model.fit(X, treated) + _fit(propensity_score_model, X, treated, coords) return self def predict_cate(self, X): @@ -338,8 +342,8 @@ def __init__( propensity_score_model = LogisticRegression(penalty=None) if model is not None: - treated_model = clone(model) - untreated_model = clone(model) + treated_model = deepcopy(model) + untreated_model = deepcopy(model) # Estimate response function self.models = { @@ -347,8 +351,9 @@ def __init__( "untreated": untreated_model, "propensity": propensity_score_model } - - self.fit(X, y, treated) + + COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + self.fit(X, y, treated, coords=COORDS) # Estimate CATE g = self.models["propensity"].predict_proba(X)[:, 1] @@ -366,11 +371,11 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): treated_model, untreated_model, propensity_score_model = self.models.values() # Estimate response functions - treated_model.fit(X_t, y_t) - untreated_model.fit(X_u, y_u) + _fit(treated_model, X_t, y_t, coords) + _fit(untreated_model, X_u, y_u, coords) # Fit propensity score model - propensity_score_model.fit(X, treated) + _fit(propensity_score_model, X, treated, coords) return self From f9d98176d63dbe2cbf565400113b2eb2bdda1e55 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 2 Mar 2023 14:09:27 +0100 Subject: [PATCH 16/57] minor changes --- causalpy/pymc_meta_learners.py | 13 +++++++++---- causalpy/skl_meta_learners.py | 5 ++++- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index 66464359..fe096d7d 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -8,6 +8,7 @@ class BayesianMetaLearner: "Base class for PyMC based meta-learners." + def plot(self): # TODO pass @@ -28,7 +29,9 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: pred_treated = m.predict(X_treated)["posterior_predictive"].mu pred_untreated = m.predict(X_untreated)["posterior_predictive"].mu - return pred_treated - pred_untreated + self.cate_posterior = pred_treated - pred_untreated + + return self.cate_posterior.mean(dim=["chain", "draw"]) class BayesianTLearner(BayesianMetaLearner, TLearner): @@ -41,7 +44,9 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: pred_treated = treated_model.predict(X)["posterior_predictive"].mu pred_untreated = untreated_model.predict(X)["posterior_predictive"].mu - return pred_treated - pred_untreated + self.cate_posterior = pred_treated - pred_untreated + + return self.cate_posterior.mean(dim=["chain", "draw"]) class BayesianXLearner(BayesianMetaLearner, XLearner): @@ -54,7 +59,7 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: class BayesianDRLearner(BayesianMetaLearner, DRLearner): "PyMC version of DR-Learner." - + def predict_cate(self, X: pd.DataFrame) -> np.array: # TODO - pass \ No newline at end of file + pass diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 2bfcc4d6..7b62fd7d 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -130,7 +130,10 @@ def __init__( super().__init__(X=X, y=y, treated=treated) self.models['model'] = model - COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + COORDS = { + "coeffs": list(X.columns) + ["treated"], + "obs_indx": np.arange(X.shape[0]) + } self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) From 9917a8365683a2f1407216180cecc48214898c6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 2 Mar 2023 16:00:56 +0100 Subject: [PATCH 17/57] continuing to integrate pymc models --- causalpy/pymc_meta_learners.py | 30 +++++++++-------- causalpy/skl_meta_learners.py | 59 ++++++++++++++++++++-------------- 2 files changed, 51 insertions(+), 38 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index fe096d7d..84adfc7e 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -3,19 +3,15 @@ import pymc as pm -from causalpy.skl_meta_learners import SLearner, TLearner, XLearner, DRLearner +from causalpy.skl_meta_learners import MetaLearner, SLearner, TLearner, XLearner, DRLearner -class BayesianMetaLearner: +class BayesianMetaLearner(MetaLearner): "Base class for PyMC based meta-learners." - def plot(self): - # TODO - pass - - def summary(self): - # TODO - pass + def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: + super().__init__(X, y, treated) + self.expected_cate = self.cate.mean(dim=["chain", "draw"]) class BayesianSLearner(BayesianMetaLearner, SLearner): @@ -24,14 +20,14 @@ class BayesianSLearner(BayesianMetaLearner, SLearner): def predict_cate(self, X: pd.DataFrame) -> np.array: X_untreated = X.assign(treatment=0) X_treated = X.assign(treatment=1) - m = self.models['model'] + m = self.models["model"] pred_treated = m.predict(X_treated)["posterior_predictive"].mu pred_untreated = m.predict(X_untreated)["posterior_predictive"].mu - self.cate_posterior = pred_treated - pred_untreated + cate = pred_treated - pred_untreated - return self.cate_posterior.mean(dim=["chain", "draw"]) + return cate class BayesianTLearner(BayesianMetaLearner, TLearner): @@ -44,14 +40,20 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: pred_treated = treated_model.predict(X)["posterior_predictive"].mu pred_untreated = untreated_model.predict(X)["posterior_predictive"].mu - self.cate_posterior = pred_treated - pred_untreated + cate = pred_treated - pred_untreated - return self.cate_posterior.mean(dim=["chain", "draw"]) + return cate class BayesianXLearner(BayesianMetaLearner, XLearner): "PyMC version of X-Learner." + def _compute_cate(self, X): + cate_t = self.models["treated_cate"].predict(X)["posterior_predictive"].mu + cate_u = self.models["treated_cate"].predict(X)["posterior_predictive"].mu + g = self.models["propensity"].predict(X)["posterior_predictive"].mu + return g * cate_u + (1 - g) * cate_t + def predict_cate(self, X: pd.DataFrame) -> np.array: # TODO pass diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 7b62fd7d..ca31d894 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -15,7 +15,7 @@ def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: # Check whether input is appropriate check_consistent_length(X, y, treated) if not _is_variable_dummy_coded(treated): - raise ValueError('Treatment variable is not dummy coded.') + raise ValueError("Treatment variable is not dummy coded.") self.cate = None self.treated = treated @@ -31,6 +31,23 @@ def predict_ate(self, X: pd.DataFrame) -> np.float64: "Predict average treatment effect for given input X." return self.predict_cate(X).mean() + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + "Fits model." + raise NotImplementedError() + + def summary(self): + "Prints summary." + print(f"Number of observations: {self.X.shape[0]}") + print(f"Number of treated observations: {self.treated.sum()}") + print(f"Average treatement effect (ATE): {self.predict_ate(self.X.shape[0])}") + + def plot(self): + "Plots results. Content is undecided yet." + plt.hist(self.cate, bins=40, density=True) + + +class SkMetaLearner(MetaLearner): + "Base class for sklearn based meta-learners." def bootstrap( self, X_ins: pd.DataFrame, @@ -95,25 +112,16 @@ def cate_confidence_interval( axis=1, ) return conf_ints - - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): - "Fits model." - raise NotImplementedError() - + def summary(self): - "Prints summary." conf_ints = self.ate_confidence_interval(self.X, self.y, self.treated, self.X) print(f"Number of observations: {self.X.shape[0]}") print(f"Number of treated observations: {self.treated.sum()}") print(f"Average treatement effect (ATE): {self.predict_ate(self.X.shape[0])}") print(f"Confidence interval for ATE: {conf_ints}") - def plot(self): - "Plots results. Content is undecided yet." - plt.hist(self.cate, bins=40, density=True) - -class SLearner(MetaLearner): +class SLearner(SkMetaLearner): """ Implements of S-learner described in [1]. S-learner estimates conditional average treatment effect with the use of a single model. @@ -128,7 +136,7 @@ def __init__( self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, model ) -> None: super().__init__(X=X, y=y, treated=treated) - self.models['model'] = model + self.models["model"] = model COORDS = { "coeffs": list(X.columns) + ["treated"], @@ -140,17 +148,17 @@ def __init__( def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): X_t = X.assign(treatment=treated) - _fit(model=self.models['model'], X=X_t, y=y, coords=coords) + _fit(model=self.models["model"], X=X_t, y=y, coords=coords) return self def predict_cate(self, X: pd.DataFrame) -> np.array: X_control = X.assign(treatment=0) X_treated = X.assign(treatment=1) - m = self.models['model'] + m = self.models["model"] return m.predict(X_treated) - m.predict(X_control) -class TLearner(MetaLearner): +class TLearner(SkMetaLearner): """ Implements of T-learner described in [1]. T-learner fits two separate models to estimate conditional average treatment effect. @@ -207,7 +215,7 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: return treated_model.predict(X) - untreated_model.predict(X) -class XLearner(MetaLearner): +class XLearner(SkMetaLearner): """ Implements of X-learner introduced in [1]. X-learner estimates conditional average treatment effect with the use of five separate models. @@ -265,11 +273,14 @@ def __init__( self.fit(X, y, treated, coords=COORDS) # Compute cate - cate_t = treated_cate_estimator.predict(X) - cate_u = treated_cate_estimator.predict(X) - g = self.models["propensity"].predict(X) + self.cate = self._compute_cate(X) - self.cate = g * cate_u + (1 - g) * cate_t + def _compute_cate(self, X): + "Predicts with models and then computes cate." + cate_t = self.models["treated_cate"].predict(X) + cate_u = self.models["treated_cate"].predict(X) + g = self.models["propensity"].predict_proba(X) + return g * cate_u + (1 - g) * cate_t def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): ( @@ -292,8 +303,8 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): tau_u = treated_model.predict(X_u) - y_u # Estimate CATE separately on treated and untreated subsets - treated_cate_estimator.fit(X_t, tau_t) - untreated_cate_estimator.fit(X_u, tau_u) + _fit(treated_cate_estimator, X_t, tau_t, coords) + _fit(untreated_cate_estimator, X_u, tau_u, coords) # Fit propensity score model _fit(propensity_score_model, X, treated, coords) @@ -308,7 +319,7 @@ def predict_cate(self, X): return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated -class DRLearner(MetaLearner): +class DRLearner(SkMetaLearner): """ Implements of DR-learner also known as doubly robust learner as described in [1]. From a8d64672cf0b059e18fd916ee4ac0b9b00a2fc7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 2 Mar 2023 16:03:32 +0100 Subject: [PATCH 18/57] bugfix --- causalpy/skl_meta_learners.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index ca31d894..b88cd93d 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -279,7 +279,7 @@ def _compute_cate(self, X): "Predicts with models and then computes cate." cate_t = self.models["treated_cate"].predict(X) cate_u = self.models["treated_cate"].predict(X) - g = self.models["propensity"].predict_proba(X) + g = self.models["propensity"].predict_proba(X)[:, 1] return g * cate_u + (1 - g) * cate_t def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): From 55b43df24e0dd90935412255a1045018a9b5afe6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 2 Mar 2023 16:10:32 +0100 Subject: [PATCH 19/57] more minor bugfixes --- causalpy/pymc_meta_learners.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index 84adfc7e..b7b48a65 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -11,10 +11,9 @@ class BayesianMetaLearner(MetaLearner): def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: super().__init__(X, y, treated) - self.expected_cate = self.cate.mean(dim=["chain", "draw"]) -class BayesianSLearner(BayesianMetaLearner, SLearner): +class BayesianSLearner(SLearner, BayesianMetaLearner): "PyMC version of S-Learner." def predict_cate(self, X: pd.DataFrame) -> np.array: @@ -30,7 +29,7 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: return cate -class BayesianTLearner(BayesianMetaLearner, TLearner): +class BayesianTLearner(TLearner, BayesianMetaLearner): "PyMC version of T-Learner." def predict_cate(self, X: pd.DataFrame) -> np.array: @@ -45,7 +44,7 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: return cate -class BayesianXLearner(BayesianMetaLearner, XLearner): +class BayesianXLearner(XLearner, BayesianMetaLearner): "PyMC version of X-Learner." def _compute_cate(self, X): @@ -59,7 +58,7 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: pass -class BayesianDRLearner(BayesianMetaLearner, DRLearner): +class BayesianDRLearner(DRLearner, BayesianMetaLearner): "PyMC version of DR-Learner." def predict_cate(self, X: pd.DataFrame) -> np.array: From 9d5bb61ad97e702cbda405c33c8ac5bed9ed6f09 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 2 Mar 2023 17:07:04 +0100 Subject: [PATCH 20/57] added logistic regression --- causalpy/pymc_models.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index d9efc51a..aa1c7919 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -130,4 +130,15 @@ def build_model(self, X, y, coords=None): self.add_coords(coords) X = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) mu = pmb.BART("mu", X, y, m=self.m, dims="obs_ind") - pm.Normal("y_hat", mu=mu, sigma=self.sigma, observed=y, dims="obs_ind") \ No newline at end of file + pm.Normal("y_hat", mu=mu, sigma=self.sigma, observed=y, dims="obs_ind") + +class LogisticRegression(ModelBuilder): + "Custom PyMC model for logistic regression." + + def build_model(self, X, y, coords) -> None: + with self: + self.add_coords(coords) + X = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) + beta = pm.Normal("beta", 0, 50, dims="coeffs") + mu = pm.math.sigmoid(pm.math.dot(X, beta)) + pm.Bernoulli("yhat", mu, observed=y) From 3f77e7659a565942b52d13adbf36b244099e67f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sat, 4 Mar 2023 09:02:01 +0100 Subject: [PATCH 21/57] added bayesian DRLearner --- causalpy/pymc_meta_learners.py | 61 ++++++++++++++++++++++++++++++---- causalpy/skl_meta_learners.py | 20 +++++++---- 2 files changed, 68 insertions(+), 13 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index b7b48a65..dbcc8e21 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -4,13 +4,16 @@ from causalpy.skl_meta_learners import MetaLearner, SLearner, TLearner, XLearner, DRLearner - +from causalpy.pymc_models import LogisticRegression class BayesianMetaLearner(MetaLearner): "Base class for PyMC based meta-learners." - def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: - super().__init__(X, y, treated) + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + "Fits model." + raise NotImplementedError() + + class BayesianSLearner(SLearner, BayesianMetaLearner): @@ -47,6 +50,31 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: class BayesianXLearner(XLearner, BayesianMetaLearner): "PyMC version of X-Learner." + def __init__( + self, + X, + y, + treated, + model=None, + treated_model=None, + untreated_model=None, + treated_cate_estimator=None, + untreated_cate_estimator=None, + propensity_score_model=LogisticRegression() + ) -> None: + + super().__init__( + X, + y, + treated, + model, + treated_model, + untreated_model, + treated_cate_estimator, + untreated_cate_estimator, + propensity_score_model + ) + def _compute_cate(self, X): cate_t = self.models["treated_cate"].predict(X)["posterior_predictive"].mu cate_u = self.models["treated_cate"].predict(X)["posterior_predictive"].mu @@ -54,13 +82,32 @@ def _compute_cate(self, X): return g * cate_u + (1 - g) * cate_t def predict_cate(self, X: pd.DataFrame) -> np.array: - # TODO - pass + treated_model = self.models["treated_cate"] + untreated_model = self.models["untreated_cate"] + + cate_estimate_treated = treated_model.predict(X)["posterior_predictive"].mu + cate_estimate_untreated = untreated_model.predict(X)["posterior_predictive"].mu + g = self.models["propensity"].predict(X)["posterior_predictive"].mu + + return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated class BayesianDRLearner(DRLearner, BayesianMetaLearner): "PyMC version of DR-Learner." + def __init__( + self, + X, + y, + treated, + model=None, + treated_model=None, + untreated_model=None, + propensity_score_model=LogisticRegression() + ): + super().__init__(X, y, treated, model, treated_model, untreated_model, propensity_score_model) + def predict_cate(self, X: pd.DataFrame) -> np.array: - # TODO - pass + m1 = self.models["treated"].predict(X) + m0 = self.models["untreated"].predict(X) + return m1 - m0 \ No newline at end of file diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index b88cd93d..e6476405 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -24,12 +24,23 @@ def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: self.models = {} def predict_cate(self, X: pd.DataFrame) -> np.array: - "Predict conditional average treatment effect for given input X." + """ + Predict out-of-sample conditional average treatment effect on given input X. + For in-sample treatement effect self.cate should be used. + """ raise NotImplementedError() def predict_ate(self, X: pd.DataFrame) -> np.float64: - "Predict average treatment effect for given input X." + """ + Predict out-of-sample average treatment effect on given input X. For in-sample treatement + effect self.ate() should be used. + """ return self.predict_cate(X).mean() + + + def ate(self): + "Returns in-sample average treatement effect." + return self.cate.mean() def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): "Fits model." @@ -236,7 +247,7 @@ def __init__( untreated_model=None, treated_cate_estimator=None, untreated_cate_estimator=None, - propensity_score_model=None + propensity_score_model=LogisticRegression(penalty=None) ): super().__init__(X=X, y=y, treated=treated) @@ -251,9 +262,6 @@ def __init__( treated_cate_estimator, untreated_cate_estimator has to be specified." ) - if propensity_score_model is None: - propensity_score_model = LogisticRegression(penalty=None) - if model is not None: treated_model = deepcopy(model) untreated_model = deepcopy(model) From faf0db520c37169100c495b5f643eee4e8cbc6e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 5 Mar 2023 23:57:42 +0100 Subject: [PATCH 22/57] fixed some issues with X and DR learners --- causalpy/pymc_meta_learners.py | 66 ++++++++++++++++++++++++++++++++-- causalpy/skl_meta_learners.py | 20 ++++++----- 2 files changed, 76 insertions(+), 10 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index dbcc8e21..da96f896 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -1,8 +1,9 @@ import pandas as pd import numpy as np import pymc as pm +import xarray as xa - +from causalpy.utils import _fit from causalpy.skl_meta_learners import MetaLearner, SLearner, TLearner, XLearner, DRLearner from causalpy.pymc_models import LogisticRegression @@ -74,6 +75,50 @@ def __init__( untreated_cate_estimator, propensity_score_model ) + + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + ( + treated_model, + untreated_model, + treated_cate_estimator, + untreated_cate_estimator, + propensity_score_model + ) = self.models.values() + + # Split data to treated and untreated subsets + X_t, y_t = X[treated == 1], y[treated == 1] + X_u, y_u = X[treated == 0], y[treated == 0] + + # Estimate response function + _fit(treated_model, X_t, y_t, coords) + _fit(untreated_model, X_u, y_u, coords) + + pred_u_t = ( + untreated_model + .predict(X_t)["posterior_predictive"] + .mu + .mean(dim=["chain", "draw"]) + .to_numpy() + ) + pred_t_u = ( + treated_model + .predict(X_u)["posterior_predictive"] + .mu + .mean(dim=["chain", "draw"]) + .to_numpy() + ) + + tau_t = y_t - pred_u_t + tau_u = y_u - pred_t_u + + # Estimate CATE separately on treated and untreated subsets + _fit(treated_cate_estimator, X_t, tau_t, coords) + _fit(untreated_cate_estimator, X_u, tau_u, coords) + + # Fit propensity score model + _fit(propensity_score_model, X, treated, coords) + return self + def _compute_cate(self, X): cate_t = self.models["treated_cate"].predict(X)["posterior_predictive"].mu @@ -110,4 +155,21 @@ def __init__( def predict_cate(self, X: pd.DataFrame) -> np.array: m1 = self.models["treated"].predict(X) m0 = self.models["untreated"].predict(X) - return m1 - m0 \ No newline at end of file + return m1 - m0 + + def _compute_cate(self, X, y, treated): + g = self.models["propensity"].predict(X)["posterior_predictive"].mu + m0 = self.models["untreated"].predict(X)["posterior_predictive"].mu + m1 = self.models["treated"].predict(X)["posterior_predictive"].mu + + + # Broadcast target and treated variables to the size of the predictions + y0 = xa.DataArray(y, dims="obs_ind") + y0 = xa.broadcast(y0, m0)[0] + + t0 = xa.DataArray(treated, dims="obs_ind") + t0 = xa.broadcast(t0, m0)[0] + + cate = (t0 * (y0 - m1) / g + m1 - ((1 - t0) * (y0 - m0) / (1 - g) + m0)) + + return cate \ No newline at end of file diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index e6476405..a60f2136 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -284,7 +284,7 @@ def __init__( self.cate = self._compute_cate(X) def _compute_cate(self, X): - "Predicts with models and then computes cate." + "Computes cate for given input." cate_t = self.models["treated_cate"].predict(X) cate_u = self.models["treated_cate"].predict(X) g = self.models["propensity"].predict_proba(X)[:, 1] @@ -345,7 +345,7 @@ def __init__( model=None, treated_model=None, untreated_model=None, - propensity_score_model=None + propensity_score_model=LogisticRegression(penalty=None) ): super().__init__(X=X, y=y, treated=treated) @@ -360,8 +360,6 @@ def __init__( treated_cate_estimator, untreated_cate_estimator has to be specified." ) - if propensity_score_model is None: - propensity_score_model = LogisticRegression(penalty=None) if model is not None: treated_model = deepcopy(model) @@ -378,12 +376,18 @@ def __init__( self.fit(X, y, treated, coords=COORDS) # Estimate CATE + self.cate = self._compute_cate(X, y, treated) + + + def _compute_cate(self, X, y, treated): g = self.models["propensity"].predict_proba(X)[:, 1] - m0 = untreated_model.predict(X) - m1 = treated_model.predict(X) + m0 = self.models["untreated"].predict(X) + m1 = self.models["treated"].predict(X) + + cate = (treated * (y - m1) / g + m1 + - ((1 - treated) * (y - m0) / (1 - g) + m0)) - self.cate = (treated * (y - m1) / g + m1 - - ((1 - treated) * (y - m0) / (1 - g) + m0)) + return cate def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): # Split data to treated and untreated subsets From c1bbf33daa024bd8603f83b6c976681f83fe70ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 6 Mar 2023 20:14:44 +0100 Subject: [PATCH 23/57] small bugfixes --- causalpy/skl_meta_learners.py | 103 +++++++++++++++++++++------------- 1 file changed, 64 insertions(+), 39 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index a60f2136..78d79de8 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -36,8 +36,7 @@ def predict_ate(self, X: pd.DataFrame) -> np.float64: effect self.ate() should be used. """ return self.predict_cate(X).mean() - - + def ate(self): "Returns in-sample average treatement effect." return self.cate.mean() @@ -59,27 +58,47 @@ def plot(self): class SkMetaLearner(MetaLearner): "Base class for sklearn based meta-learners." + def bootstrap( self, X_ins: pd.DataFrame, y: pd.Series, treated: pd.Series, - X: None, - frac_samples: float = None, - n_samples: int = 1000, + X: pd.DataFrame = None, n_iter: int = 1000 ) -> np.array: """ Runs bootstrap n_iter times on a sample of size n_samples. Fits on (X_ins, y, treated), then predicts on X. """ + if X is None: + X = X_ins + # Bootstraping overwrites these attributes models, cate = self.models, self.cate + + # Calculate number of treated and untreated data points + n1 = self.treated.sum() + n0 = self.treated.count() - n1 + + # Prescribed treatement variable of samples + t_bs = pd.Series(n0 * [0] + n1 * [1], name="treatement") + results = [] + + # TODO: paralellize this loop for _ in range(n_iter): - X_bs = X_ins.sample(frac=frac_samples, n=n_samples, replace=True) - y_bs = y.loc[X_bs.index].reset_index(drop=True) - t_bs = treated.loc[X_bs.index].reset_index(drop=True) + # Take sample with replacement from our data in a way that we have + # the same number of treated and untreated data points as in the whole + # data set. + X0_bs = X_ins[treated == 0].sample(n=n0, replace=True) + y0_bs = y.loc[X0_bs.index] + + X1_bs = X_ins[treated == 1].sample(n=n1, replace=True) + y1_bs = y.loc[X1_bs.index] + + X_bs = pd.concat([X0_bs, X1_bs], axis=0).reset_index(drop=True) + y_bs = pd.concat([y0_bs, y1_bs], axis=0).reset_index(drop=True) self.fit(X_bs.reset_index(drop=True), y_bs, t_bs) results.append(self.predict_cate(X)) @@ -93,42 +112,50 @@ def ate_confidence_interval( X_ins: pd.DataFrame, y: pd.Series, treated: pd.Series, - X: None, - q: float = .95, - frac_samples: float = None, - n_samples: int = 1000, + X: pd.DataFrame = None, + q: float = .05, n_iter: int = 1000 - ): + ) -> tuple: "Estimates confidence intervals for ATE on X using bootstraping." - cates = self.bootstrap(X_ins, y, treated, X, frac_samples, n_samples, n_iter) - return np.quantile(cates, q=q), np.quantile(cates, q=1 - q) - + cates = self.bootstrap(X_ins, y, treated, X, n_iter) + ates = cates.mean(axis=0) + return np.quantile(ates, q=q / 2), np.quantile(cates, q=1 - q / 2) def cate_confidence_interval( self, X_ins: pd.DataFrame, y: pd.Series, treated: pd.Series, - X: None, - q: float = .95, - frac_samples: float = None, - n_samples: int = 1000, + X: pd.DataFrame = None, + q: float = .05, n_iter: int = 1000 - ): + ) -> np.array: "Estimates confidence intervals for CATE on X using bootstraping." - cates = self.bootstrap(X_ins, y, treated, X, frac_samples, n_samples, n_iter) + cates = self.bootstrap(X_ins, y, treated, X, n_iter) conf_ints = np.append( - np.quantile(cates, q, axis=0).reshape(-1, 1), - np.quantile(cates, 1 - q, axis=0).reshape(-1, 1), + np.quantile(cates, q / 2, axis=0).reshape(-1, 1), + np.quantile(cates, 1 - q / 2, axis=0).reshape(-1, 1), axis=1, ) return conf_ints - - def summary(self): - conf_ints = self.ate_confidence_interval(self.X, self.y, self.treated, self.X) + + def bias( + self, + X_ins: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + X: pd.DataFrame = None, + q: float = .05, + n_iter: int = 1000 + ): + cates = self.bootstrap(X_ins, y, treated, X, n_iter) + + def summary(self, n_iter=1000): + conf_ints = self.ate_confidence_interval( + self.X, self.y, self.treated, self.X, n_iter=n_iter) print(f"Number of observations: {self.X.shape[0]}") print(f"Number of treated observations: {self.treated.sum()}") - print(f"Average treatement effect (ATE): {self.predict_ate(self.X.shape[0])}") + print(f"Average treatement effect (ATE): {self.predict_ate(self.X)}") print(f"Confidence interval for ATE: {conf_ints}") @@ -152,7 +179,7 @@ def __init__( COORDS = { "coeffs": list(X.columns) + ["treated"], "obs_indx": np.arange(X.shape[0]) - } + } self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) @@ -195,12 +222,12 @@ def __init__( raise ValueError( "Either model or both of treated_model and untreated_model \ have to be specified." - ) + ) elif not (model is None or untreated_model is None or treated_model is None): raise ValueError( "Either model or both of treated_model and untreated_model \ have to be specified." - ) + ) if model is not None: untreated_model = deepcopy(model) @@ -273,7 +300,7 @@ def __init__( "untreated": untreated_model, "treated_cate": treated_cate_estimator, "untreated_cate": untreated_cate_estimator, - "propensity": propensity_score_model + "propensity": propensity_score_model } COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} @@ -353,13 +380,12 @@ def __init__( raise ValueError( "Either model or each of treated_model, untreated_model, \ treated_cate_estimator, untreated_cate_estimator has to be specified." - ) + ) elif not (model is None or untreated_model is None or treated_model is None): raise ValueError( "Either model or each of treated_model, untreated_model, \ treated_cate_estimator, untreated_cate_estimator has to be specified." - ) - + ) if model is not None: treated_model = deepcopy(model) @@ -370,15 +396,14 @@ def __init__( "treated": treated_model, "untreated": untreated_model, "propensity": propensity_score_model - } - + } + COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} self.fit(X, y, treated, coords=COORDS) # Estimate CATE self.cate = self._compute_cate(X, y, treated) - def _compute_cate(self, X, y, treated): g = self.models["propensity"].predict_proba(X)[:, 1] m0 = self.models["untreated"].predict(X) @@ -408,4 +433,4 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): def predict_cate(self, X): m1 = self.models["treated"].predict(X) m0 = self.models["untreated"].predict(X) - return m1 - m0 \ No newline at end of file + return m1 - m0 From 2f689dda2fabd196c7549a686998bc469d8ed519 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 6 Mar 2023 22:42:56 +0100 Subject: [PATCH 24/57] added (incomplete) notebook explaining meta-learners --- .../meta_learners_synthetic_data.ipynb | 492 ++++++++++++++++++ 1 file changed, 492 insertions(+) create mode 100644 docs/notebooks/meta_learners_synthetic_data.ipynb diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb new file mode 100644 index 00000000..e0a4cc40 --- /dev/null +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -0,0 +1,492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fafe7c80-fc2d-47d4-b260-c5a1082d6fbb", + "metadata": {}, + "source": [ + "# Comparing meta-learners with different ensembles of trees as base learners" + ] + }, + { + "cell_type": "markdown", + "id": "3ccc6766-7606-4b6b-8c76-b4b5b732c988", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Meta-learners are a class of models used for estimating the effectiveness of a certain treatement and they are used when the effectiveness of a treatement is assumed to be *heterogeneous*, that is the effect of the treatement might vary across individuals. In particular, they estimate the treatement effectiveness by decomposing the problem to several subregressions. The models used for these subregressions will be called *base learners*. Some of the most popular base learners are tree based ensembles, in particular Random Forest and Bayesian Additive Regression Trees (BART) due to their flexibility, however any machine learning algorithm suitable for regression problems can be used.\n", + "\n", + "Following [1], we will denote by $Y$ an outcome vector, by $X_i \\in \\mathbb{R}^d$ a feature vector containing potential confounders for the $i$th unit, by $W_i \\in \\{0, 1\\}$ the treatment assignment indicator and by $Y_i (0) \\in \\mathbb{R}$ and $Y_i(1) \\in \\mathbb{R}$ the potential outcome of the $i$th unit when assigned to the control and the treatement group respectively. With this notation\n", + "\n", + "$$ Y = W Y(1) + (1 - W) Y(0). $$\n", + "\n", + "Our task is to estimate the *conditional average treatement effect (CATE)*, defined as \n", + "$$\\tau(x) = \\mathbb{E}\\left[Y(1) - Y(0) \\mid X=x\\right].$$\n", + "\n", + "The goal of this notebook is to introduce several meta-learners and compare their performance estimating CATE. The evaluation of an estimation $\\hat{\\tau}$ of $\\tau$ will be based on *expected mean squared error* defined as \n", + "$$\\operatorname{EMSE}(\\tau) = \\mathbb{E}\\left[\\left(\\tau(X) - \\hat{\\tau}(X)\\right)^2\\right].$$" + ] + }, + { + "cell_type": "markdown", + "id": "ed73c7c9-7df5-4912-80f4-c5e69c916c22", + "metadata": {}, + "source": [ + "## The fundamental problem of causal inference\n", + "\n", + "The fact that only one of the potential outcomes ever materializes, means that CATE is never directly observed, thus on real world data, EMSE cannot be computed. This is often refered to as *the fundamental problem of causal inference*. To get around this problem, in this notebook we will deal with synthetic data with data generating process of the form\n", + "$$y = \\sigma(\\alpha X) + W \\sigma(\\beta X) + \\varepsilon,$$\n", + "where $\\varepsilon_i \\sim N(0, 1)$ and $W_i \\sim \\operatorname{Bernoulli}\\left(\\frac12\\right)$ are i.i.d. and $$\\sigma(x) = \\frac{e^x}{1 + e^x}$$ is the sigmoid function (applied coordinatewise).\n", + "Here, CATE can be expressed as $\\tau(X) = \\sigma(\\beta X)$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c543b717-acb7-4c7f-aa77-975833dbf699", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "np.random.seed(42)\n", + "\n", + "def sigmoid(x):\n", + " return np.exp(x) / (1 + np.exp(x))\n", + "\n", + "def create_synthetic_data(*shape):\n", + " X = np.random.rand(*shape)\n", + "\n", + " α = np.random.rand(shape[1])\n", + " β = np.random.rand(shape[1])\n", + "\n", + " treatment = np.random.randint(low=0, high=2, size=shape[0])\n", + " treatment_effect = sigmoid(X @ β)\n", + "\n", + " noise = np.random.rand(shape[0])\n", + "\n", + " y = sigmoid(X @ α) + treatment_effect * treatment + noise\n", + "\n", + " X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(shape[1])])\n", + " y = pd.Series(y, name='target')\n", + " treated = pd.Series(treatment, name='treatment')\n", + " return X, y, treated, treatment_effect\n", + "\n", + "synth_data = create_synthetic_data(1000, 6)\n", + "\n", + "(\n", + " X_train,\n", + " X_test,\n", + " y_train,\n", + " y_test,\n", + " treated_train,\n", + " treated_test,\n", + " effect_train,\n", + " effect_test\n", + ") = train_test_split(*synth_data, test_size=.5, stratify=synth_data[2])" + ] + }, + { + "cell_type": "markdown", + "id": "9cf837e1-d93c-4fc5-bc52-96296db82e51", + "metadata": { + "tags": [] + }, + "source": [ + "## S-learner\n", + "\n", + "The simplest meta-learner is the *S-learner*, which treats the treatement assignment indicator as a feature and estimates \n", + "\n", + "$$\\mu(x, w) := \\mathbb{E}[Y \\mid X=x, W=w]$$\n", + "\n", + "with an appropriate base learner. The \"S\" in S-learner stands for \"single\", as it uses a single model to estimate CATE. We will denote this estimation with $\\hat{\\mu}(x, w)$. Then the S-learner approximates CATE as \n", + "\n", + "$$\\hat{\\tau_S}(x) = \\hat{\\mu}(x, 1) - \\hat{\\mu}(x, 0).$$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "454322d0-6dbc-4771-bb7a-344afa30e793", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 260\n", + "Average treatement effect (ATE): 0.7934167385101318\n", + "95% Confidence interval for ATE: (0.48431164473295213, 1.2554474145174028)\n", + "Estimated bias: 0.01015559397637844\n", + "Actual average treatement effect: 0.8166310319237063\n", + "EMSE: 0.04852673729489554\n" + ] + } + ], + "source": [ + "from xgboost import XGBRegressor\n", + "from skl_meta_learners import SLearner\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "slearner_xgb = SLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", + "\n", + "\n", + "slearner_xgb.summary(n_iter=100)\n", + "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", + "print(f\"EMSE: {mean_squared_error(effect_train, slearner_xgb.cate)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d552d0e9-5d6c-4d4b-bde8-80c680a51e62", + "metadata": {}, + "source": [ + "**TODO: Do some plotting. As of now it plots the distribution of the CATE estimates. While that is not totally uninteresting, it is not good either.**\n", + "\n", + "More ideas: \n", + "- feature to estimated CATE scatter plot?\n", + "- several SHAP related things?\n", + "- ...?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "059fd652-3fa9-42d3-b639-a48d96263314", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "slearner_xgb.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3fe391b5-d8df-438f-ace2-0406d19e5813", + "metadata": {}, + "source": [ + "Eventough, the S-learner performs quite well in our synthetic example, it has some limitations. Most importantly, since the treatement indicator is included in the regression as an ordinary variable, it's effect may get lost, especially if the base learner is a tree based ensemble." + ] + }, + { + "cell_type": "markdown", + "id": "c3d35f7f-6526-4b7d-ac36-2365fc08cb5d", + "metadata": {}, + "source": [ + "## T-learner" + ] + }, + { + "cell_type": "markdown", + "id": "54a8971c-5eef-4423-893e-9277e8a40ac1", + "metadata": {}, + "source": [ + "The *T-learner* solves the issue mentioned above by predicting $Y(0)$ and $Y(1)$ in two different regressions. Namely, we estimate\n", + "$$\\mu_{\\operatorname{treated}}(x) := \\mathbb{E}[Y(1) \\mid X=x], \\quad \\text{and} \\quad \\mu_{\\operatorname{untreated}}(x) := \\mathbb{E}[Y(0) \\mid X=x]$$\n", + "separately and define estimate the cate as \n", + "$$\\hat{\\tau}_T(x) = \\mu_{\\operatorname{treated}}(x) - \\mu_{\\operatorname{untreated}}(x).$$ " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2a24a76e-398a-4629-9d49-7fadecb2a2c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 260\n", + "Average treatement effect (ATE): 0.7737631797790527\n", + "95% Confidence interval for ATE: (0.39325197115540506, 1.380606359243393)\n", + "Estimated bias: 0.0014979877742007375\n", + "Actual average treatement effect: 0.8166310319237063\n", + "EMSE: 0.10909539371677517\n" + ] + } + ], + "source": [ + "from skl_meta_learners import TLearner\n", + "\n", + "tlearner_xgb = TLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", + "\n", + "\n", + "tlearner_xgb.summary(n_iter=100)\n", + "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", + "print(f\"EMSE: {mean_squared_error(effect_train, tlearner_xgb.cate)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7bef7acd-3d29-4b2a-b364-a915a30f4f46", + "metadata": {}, + "source": [ + "# X-learner\n", + "The X-learner ..." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "250bd516-b1ad-452f-b26c-e0eafc3747f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 260\n", + "Average treatement effect (ATE): 0.7781170069093923\n", + "95% Confidence interval for ATE: (0.5208629256497909, 1.211836616459581)\n", + "Estimated bias: 0.005161760576251569\n", + "Actual average treatement effect: 0.8166310319237063\n", + "EMSE: 0.06922727984762232\n" + ] + } + ], + "source": [ + "from skl_meta_learners import XLearner\n", + "\n", + "xlearner_xgb = XLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", + "\n", + "\n", + "xlearner_xgb.summary(n_iter=100)\n", + "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", + "print(f\"EMSE: {mean_squared_error(effect_train, xlearner_xgb.cate)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6a499cc5-2fcf-4016-978b-c3c28c861d5a", + "metadata": {}, + "source": [ + "# DR-learner\n", + "The DR-learner ..." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "687aa584-b8af-4863-a7cf-570e86080057", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 260\n", + "Average treatement effect (ATE): 0.7738259434700012\n", + "95% Confidence interval for ATE: (0.38628790229558946, 1.3743409484624864)\n", + "Estimated bias: -0.002300022169947624\n", + "Actual average treatement effect: 0.8166310319237063\n", + "EMSE: 0.10963045326355204\n" + ] + } + ], + "source": [ + "from skl_meta_learners import DRLearner\n", + "\n", + "drlearner_xgb = DRLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", + "\n", + "\n", + "drlearner_xgb.summary(n_iter=100)\n", + "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", + "print(f\"EMSE: {mean_squared_error(effect_train, drlearner_xgb.cate)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "648ef110-5a67-4eca-8ff0-0ddb0308c866", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor, AdaBoostRegressor\n", + "from lightgbm import LGBMRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a2cc2c39-2690-4943-90a9-b90fd2a60972", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_emse(learner, m):\n", + " l = learner(X_train, y_train, treated_train, m)\n", + " return mean_squared_error(effect_train, l.cate)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3552a561-8051-4a51-832a-5e088a106689", + "metadata": {}, + "outputs": [], + "source": [ + "models = [\n", + " (\"Random forest\", RandomForestRegressor()),\n", + " (\"Hist gradient boosting\", HistGradientBoostingRegressor()),\n", + " (\"AdaBoost\", AdaBoostRegressor()),\n", + " (\"XGBoost\", XGBRegressor()),\n", + " (\"LightGBM\", LGBMRegressor())\n", + "]\n", + "bench = pd.DataFrame({\n", + " m_name: {\n", + " 'SLearner': compute_emse( SLearner, m),\n", + " 'TLearner': compute_emse( TLearner, m),\n", + " 'XLearner': compute_emse( XLearner, m),\n", + " 'DRLearner': compute_emse(DRLearner, m)\n", + " } for m_name, m in models\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7eed6094-c940-4b5b-8294-bd2ffeab2eac", + "metadata": {}, + "outputs": [ + { 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SLearner0.0432660.0287200.0054360.0485270.030729
TLearner0.0453080.0559680.0115860.1090950.052863
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" + ], + "text/plain": [ + " Random forest Hist gradient boosting AdaBoost XGBoost LightGBM\n", + "SLearner 0.043266 0.028720 0.005436 0.048527 0.030729\n", + "TLearner 0.045308 0.055968 0.011586 0.109095 0.052863\n", + "XLearner 0.027551 0.042998 0.008828 0.069227 0.041362\n", + "DRLearner 0.164998 0.179821 0.279397 0.109630 0.184410" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bench" + ] + }, + { + "cell_type": "markdown", + "id": "093a6965-0ef3-4d7b-93d8-138b7602517a", + "metadata": {}, + "source": [ + "# TODO: EMSE as a function of population size" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b57e31a0a91fa78f892d063710a53aa7be27ede5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Tue, 7 Mar 2023 14:56:49 +0100 Subject: [PATCH 25/57] wrote section on X-learner --- .../meta_learners_synthetic_data.ipynb | 281 ++++++++++++------ 1 file changed, 184 insertions(+), 97 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index e0a4cc40..0c73135e 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -36,26 +36,25 @@ "## The fundamental problem of causal inference\n", "\n", "The fact that only one of the potential outcomes ever materializes, means that CATE is never directly observed, thus on real world data, EMSE cannot be computed. This is often refered to as *the fundamental problem of causal inference*. To get around this problem, in this notebook we will deal with synthetic data with data generating process of the form\n", - "$$y = \\sigma(\\alpha X) + W \\sigma(\\beta X) + \\varepsilon,$$\n", - "where $\\varepsilon_i \\sim N(0, 1)$ and $W_i \\sim \\operatorname{Bernoulli}\\left(\\frac12\\right)$ are i.i.d. and $$\\sigma(x) = \\frac{e^x}{1 + e^x}$$ is the sigmoid function (applied coordinatewise).\n", + "$$y = 10\\sigma(\\alpha X) + W \\sigma(\\beta X) + \\varepsilon,$$\n", + "where $\\varepsilon_i \\sim N(0, 1)$ and $W_i \\sim \\operatorname{Bernoulli}\\left(\\frac14\\right)$ are i.i.d. and $$\\sigma(x) = \\frac{e^x}{1 + e^x}$$ is the sigmoid function (applied coordinatewise).\n", "Here, CATE can be expressed as $\\tau(X) = \\sigma(\\beta X)$." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, "id": "c543b717-acb7-4c7f-aa77-975833dbf699", "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", + "from causalpy.skl_meta_learners import SLearner, TLearner, XLearner, DRLearner\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.ensemble import HistGradientBoostingRegressor\n", "\n", "np.random.seed(42)\n", "\n", @@ -68,30 +67,19 @@ " α = np.random.rand(shape[1])\n", " β = np.random.rand(shape[1])\n", "\n", - " treatment = np.random.randint(low=0, high=2, size=shape[0])\n", + " treatment = np.random.binomial(n=1, p=.25, size=shape[0])\n", " treatment_effect = sigmoid(X @ β)\n", "\n", " noise = np.random.rand(shape[0])\n", "\n", - " y = sigmoid(X @ α) + treatment_effect * treatment + noise\n", + " y = 10 * sigmoid(X @ α) + treatment_effect * treatment + noise\n", "\n", " X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(shape[1])])\n", " y = pd.Series(y, name='target')\n", " treated = pd.Series(treatment, name='treatment')\n", " return X, y, treated, treatment_effect\n", "\n", - "synth_data = create_synthetic_data(1000, 6)\n", - "\n", - "(\n", - " X_train,\n", - " X_test,\n", - " y_train,\n", - " y_test,\n", - " treated_train,\n", - " treated_test,\n", - " effect_train,\n", - " effect_test\n", - ") = train_test_split(*synth_data, test_size=.5, stratify=synth_data[2])" + "X, y, treated, treatment_effect = create_synthetic_data(1000, 10)" ] }, { @@ -109,47 +97,49 @@ "\n", "with an appropriate base learner. The \"S\" in S-learner stands for \"single\", as it uses a single model to estimate CATE. We will denote this estimation with $\\hat{\\mu}(x, w)$. Then the S-learner approximates CATE as \n", "\n", - "$$\\hat{\\tau_S}(x) = \\hat{\\mu}(x, 1) - \\hat{\\mu}(x, 0).$$" + "$$\\hat{\\tau_S}(x) = \\hat{\\mu}(x, 1) - \\hat{\\mu}(x, 0).$$\n", + "\n", + "In the following example we will choose `HistGradientBoostingRegressor` as base learner." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "id": "454322d0-6dbc-4771-bb7a-344afa30e793", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 260\n", - "Average treatement effect (ATE): 0.7934167385101318\n", - "95% Confidence interval for ATE: (0.48431164473295213, 1.2554474145174028)\n", - "Estimated bias: 0.01015559397637844\n", - "Actual average treatement effect: 0.8166310319237063\n", - "EMSE: 0.04852673729489554\n" + "Number of observations: 1000\n", + "Number of treated observations: 261\n", + "Average treatement effect (ATE): 0.8825372188727141\n", + "95% Confidence interval for ATE: (0.5336478252676806, 1.3368338911656805)\n", + "Estimated bias: -0.005333370586446671\n", + "Actual average treatement effect: 0.8686759437628603\n", + "EMSE: 0.0489448259669642\n", + "CPU times: total: 8min 50s\n", + "Wall time: 1min 11s\n" ] } ], "source": [ - "from xgboost import XGBRegressor\n", - "from skl_meta_learners import SLearner\n", - "from sklearn.metrics import mean_squared_error\n", - "\n", - "slearner_xgb = SLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", - "\n", - "\n", - "slearner_xgb.summary(n_iter=100)\n", - "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", - "print(f\"EMSE: {mean_squared_error(effect_train, slearner_xgb.cate)}\")" + "%%time\n", + "# Utility for printing some statistics.\n", + "def summarize_learner(learner):\n", + " learner.summary(n_iter=100)\n", + " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", + " print(f\"EMSE: {mean_squared_error(treatment_effect, learner.cate)}\")\n", + "\n", + "slearner = SLearner(\n", + " X=X,\n", + " y=y,\n", + " treated=treated,\n", + " model=HistGradientBoostingRegressor()\n", + ")\n", + "\n", + "summarize_learner(slearner)" ] }, { @@ -167,13 +157,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "id": "059fd652-3fa9-42d3-b639-a48d96263314", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -183,7 +173,7 @@ } ], "source": [ - "slearner_xgb.plot()" + "slearner.plot()" ] }, { @@ -209,13 +199,16 @@ "source": [ "The *T-learner* solves the issue mentioned above by predicting $Y(0)$ and $Y(1)$ in two different regressions. Namely, we estimate\n", "$$\\mu_{\\operatorname{treated}}(x) := \\mathbb{E}[Y(1) \\mid X=x], \\quad \\text{and} \\quad \\mu_{\\operatorname{untreated}}(x) := \\mathbb{E}[Y(0) \\mid X=x]$$\n", - "separately and define estimate the cate as \n", - "$$\\hat{\\tau}_T(x) = \\mu_{\\operatorname{treated}}(x) - \\mu_{\\operatorname{untreated}}(x).$$ " + "separately and define estimate the cate as\n", + "$$\\hat{\\tau}_T(x) = \\hat{\\mu}_{\\operatorname{treated}}(x) - \\hat{\\mu}_{\\operatorname{untreated}}(x),$$\n", + "where $\\hat{\\mu}_{\\operatorname{treated}}$ and $\\hat{\\mu}_{\\operatorname{untreated}}$ are the estimated functions. \n", + "\n", + "Again, we will choose *both* models to be `HistGradientBoostingRegressor`." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "id": "2a24a76e-398a-4629-9d49-7fadecb2a2c7", "metadata": {}, "outputs": [ @@ -223,25 +216,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 260\n", - "Average treatement effect (ATE): 0.7737631797790527\n", - "95% Confidence interval for ATE: (0.39325197115540506, 1.380606359243393)\n", - "Estimated bias: 0.0014979877742007375\n", - "Actual average treatement effect: 0.8166310319237063\n", - "EMSE: 0.10909539371677517\n" + "Number of observations: 1000\n", + "Number of treated observations: 261\n", + "Average treatement effect (ATE): 0.8778680189940496\n", + "95% Confidence interval for ATE: (0.44254662378948145, 1.4724386927005046)\n", + "Estimated bias: 0.0075568485724106125\n", + "Actual average treatement effect: 0.8686759437628603\n", + "EMSE: 0.09250244262195867\n", + "CPU times: total: 11min 16s\n", + "Wall time: 1min 32s\n" ] } ], "source": [ - "from skl_meta_learners import TLearner\n", - "\n", - "tlearner_xgb = TLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", + "%%time\n", + "tlearner = TLearner(\n", + " X=X,\n", + " y=y,\n", + " treated=treated,\n", + " model=HistGradientBoostingRegressor()\n", + ")\n", "\n", "\n", - "tlearner_xgb.summary(n_iter=100)\n", - "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", - "print(f\"EMSE: {mean_squared_error(effect_train, tlearner_xgb.cate)}\")" + "summarize_learner(tlearner)" ] }, { @@ -250,12 +247,24 @@ "metadata": {}, "source": [ "# X-learner\n", - "The X-learner ..." + "The X-learner performs better than other CATE estimators when\n", + "- one of the treatement groups is significantly larger than the other one, \n", + "- when the CATE function has structural properties such as sparsity or smoothness, it can provably adapt [1].\n", + "\n", + "Very similarly, to the T-learner, the X-learner first estimates $\\mu_{\\operatorname{treated}}$ and $\\mu_{\\operatorname{untreated}}$. Next, it computes \n", + "\n", + "$$D(1) := Y(1) - \\hat{\\mu}_{\\operatorname{untreated}}(X(1)), \\quad \\text{and} \\quad D(0) = \\hat{\\mu}_{\\operatorname{treated}}(X(0)) - Y(0).$$\n", + "\n", + "Note that if $\\hat{\\mu}_{\\operatorname{untreated}} = \\mu_{\\operatorname{untreated}}$ and $\\hat{\\mu}_{\\operatorname{treated}} = \\mu_{\\operatorname{treated}}$, then $\\tau(x) = \\mathbb{E}[D(1) | X=x] = \\mathbb{E}[D(0) | X=x]$, so CATE can be estimated by regressing onto $D(0)$ or $D(1)$. Let us denote these estimations by $\\tau_0$ and $\\tau_1$, respectively. \n", + "\n", + "Finally, the X-learner computes a weighted average of the two CATE estimates, that is \n", + "$$\\hat{\\tau}_X(x) = g(x)\\hat{\\tau}_0 + (1 - g(x))\\hat{\\tau_1}(x),$$\n", + "for some weight function $g: \\mathbb{R}^d \\rightarrow [0, 1]$." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 29, "id": "250bd516-b1ad-452f-b26c-e0eafc3747f8", "metadata": {}, "outputs": [ @@ -263,25 +272,105 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 260\n", - "Average treatement effect (ATE): 0.7781170069093923\n", - "95% Confidence interval for ATE: (0.5208629256497909, 1.211836616459581)\n", - "Estimated bias: 0.005161760576251569\n", - "Actual average treatement effect: 0.8166310319237063\n", - "EMSE: 0.06922727984762232\n" + "Number of observations: 1000\n", + "Number of treated observations: 261\n", + "Average treatement effect (ATE): 0.8656013308211011\n", + "95% Confidence interval for ATE: (0.5670698664248546, 1.2446227193486425)\n", + "Estimated bias: -0.0031460678827127334\n", + "Actual average treatement effect: 0.8686759437628603\n", + "EMSE: 0.03875185597434108\n", + "CPU times: total: 22min 8s\n", + "Wall time: 3min 2s\n" ] } ], "source": [ - "from skl_meta_learners import XLearner\n", - "\n", - "xlearner_xgb = XLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", + "%%time\n", + "xlearner = XLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", - "\n", - "xlearner_xgb.summary(n_iter=100)\n", - "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", - "print(f\"EMSE: {mean_squared_error(effect_train, xlearner_xgb.cate)}\")" + "summarize_learner(xlearner)" + ] + }, + { + "cell_type": "markdown", + "id": "2668f04a-6a8a-495d-b14c-6a61d15ff795", + "metadata": {}, + "source": [ + "In CausalPy, we obtain $g$ as an estimation of the so-called *propensity score*, that is the conditional probability of a unit receiving treatment given $X$. By default, we use logistic regression to obtain this estimation. However, in case one of the treatement groups is much larger than the other one, it makes sense to set $g \\equiv 0$ or $g \\equiv 1$. This can be done the following way. " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "aaab018b-c95e-4c29-9163-410e39cd34de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 1000\n", + "Number of treated observations: 261\n", + "Average treatement effect (ATE): 0.8285314927602266\n", + "95% Confidence interval for ATE: (0.5295382157583198, 1.275838462466607)\n", + "Estimated bias: -0.010891457791703623\n", + "Actual average treatement effect: 0.8686759437628603\n", + "EMSE: 0.03875185597434108\n", + "CPU times: total: 22min 37s\n", + "Wall time: 3min 2s\n" + ] + } + ], + "source": [ + "%%time\n", + "from sklearn.dummy import DummyClassifier\n", + "\n", + "xlearner_mf = XLearner(\n", + " X=X,\n", + " y=y,\n", + " treated=treated,\n", + " model=HistGradientBoostingRegressor(),\n", + " propensity_score_model=DummyClassifier(strategy=\"most_frequent\")\n", + ")\n", + "\n", + "summarize_learner(xlearner_mf)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e6ae334d-c413-43ff-98b3-02e6071c4d7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 1000\n", + "Number of treated observations: 261\n", + "Average treatement effect (ATE): 0.8257963466324518\n", + "95% Confidence interval for ATE: (0.6015347065733898, 1.2304421938761037)\n", + "Estimated bias: -0.005635485886235879\n", + "Actual average treatement effect: 0.8686759437628603\n", + "EMSE: 0.03875185584711515\n", + "CPU times: total: 29min 2s\n", + "Wall time: 4min 7s\n" + ] + } + ], + "source": [ + "%%time\n", + "from xgboost import XGBClassifier\n", + "\n", + "xlearner_mf = XLearner(\n", + " X=X,\n", + " y=y,\n", + " treated=treated,\n", + " model=HistGradientBoostingRegressor(),\n", + " propensity_score_model=XGBClassifier()\n", + ")\n", + "\n", + "summarize_learner(xlearner_mf)" ] }, { @@ -295,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "id": "687aa584-b8af-4863-a7cf-570e86080057", "metadata": {}, "outputs": [ @@ -303,25 +392,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 260\n", - "Average treatement effect (ATE): 0.7738259434700012\n", - "95% Confidence interval for ATE: (0.38628790229558946, 1.3743409484624864)\n", - "Estimated bias: -0.002300022169947624\n", - "Actual average treatement effect: 0.8166310319237063\n", - "EMSE: 0.10963045326355204\n" + "Number of observations: 1000\n", + "Number of treated observations: 261\n", + "Average treatement effect (ATE): 0.8617951727930917\n", + "95% Confidence interval for ATE: (0.4540090400090874, 1.4731133930096414)\n", + "Estimated bias: 0.001972318343075466\n", + "Actual average treatement effect: 0.8686759437628603\n", + "EMSE: 0.1859824363704813\n", + "CPU times: total: 11min 25s\n", + "Wall time: 1min 32s\n" ] } ], "source": [ - "from skl_meta_learners import DRLearner\n", - "\n", - "drlearner_xgb = DRLearner(X=X_train, y=y_train, treated=treated_train, model=XGBRegressor())\n", - "\n", + "%%time\n", + "drlearner = DRLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", - "drlearner_xgb.summary(n_iter=100)\n", - "print(f\"Actual average treatement effect: {effect_train.mean()}\")\n", - "print(f\"EMSE: {mean_squared_error(effect_train, drlearner_xgb.cate)}\")" + "summarize_learner(drlearner)" ] }, { From 483d55bc6922b89159a6ff8e9615c5ddb6e1e7ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Tue, 7 Mar 2023 20:52:10 +0100 Subject: [PATCH 26/57] fixed major error in DRLearner implementation --- causalpy/skl_meta_learners.py | 79 +++++++++++++++++++++++------------ 1 file changed, 53 insertions(+), 26 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 78d79de8..fc8b630f 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -1,9 +1,10 @@ +from copy import deepcopy import matplotlib.pyplot as plt import numpy as np import pandas as pd -from copy import deepcopy from sklearn.linear_model import LogisticRegression from sklearn.utils import check_consistent_length +from sklearn.model_selection import train_test_split from causalpy.utils import _is_variable_dummy_coded, _fit @@ -86,7 +87,6 @@ def bootstrap( results = [] - # TODO: paralellize this loop for _ in range(n_iter): # Take sample with replacement from our data in a way that we have # the same number of treated and untreated data points as in the whole @@ -148,15 +148,28 @@ def bias( q: float = .05, n_iter: int = 1000 ): - cates = self.bootstrap(X_ins, y, treated, X, n_iter) + "Calculates bootstrap estimate of bias of CATE estimator." + if X is None: + X = X_ins + + pred = self.predict_cate(X=X) + bs_pred = self.bootstrap(X_ins, y, treated, X, n_iter).mean(axis=0) + + return (bs_pred - pred).mean() + + def summary(self, n_iter=1000): + # TODO: we run self.bootstrap twice independently. + bias = self.bias(self.X, self.y, self.treated, self.X, n_iter=n_iter) conf_ints = self.ate_confidence_interval( - self.X, self.y, self.treated, self.X, n_iter=n_iter) - print(f"Number of observations: {self.X.shape[0]}") - print(f"Number of treated observations: {self.treated.sum()}") - print(f"Average treatement effect (ATE): {self.predict_ate(self.X)}") - print(f"Confidence interval for ATE: {conf_ints}") + self.X, self.y, self.treated, self.X, n_iter=n_iter + ) + print(f"Number of observations: {self.X.shape[0]}") + print(f"Number of treated observations: {self.treated.sum()}") + print(f"Average treatement effect (ATE): {self.predict_ate(self.X)}") + print(f"95% Confidence interval for ATE: {conf_ints}") + print(f"Estimated bias: {bias}") class SLearner(SkMetaLearner): @@ -372,9 +385,13 @@ def __init__( model=None, treated_model=None, untreated_model=None, - propensity_score_model=LogisticRegression(penalty=None) + pseudo_outcome_model=None, + propensity_score_model=LogisticRegression(penalty=None), + cross_fitting=False ): super().__init__(X=X, y=y, treated=treated) + + self.cross_fitting = cross_fitting if model is None and (untreated_model is None or treated_model is None): raise ValueError( @@ -390,36 +407,36 @@ def __init__( if model is not None: treated_model = deepcopy(model) untreated_model = deepcopy(model) + pseudo_outcome_model = deepcopy(model) # Estimate response function self.models = { "treated": treated_model, "untreated": untreated_model, - "propensity": propensity_score_model + "propensity": propensity_score_model, + "pseudo_outcome": pseudo_outcome_model } COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} self.fit(X, y, treated, coords=COORDS) # Estimate CATE - self.cate = self._compute_cate(X, y, treated) - - def _compute_cate(self, X, y, treated): - g = self.models["propensity"].predict_proba(X)[:, 1] - m0 = self.models["untreated"].predict(X) - m1 = self.models["treated"].predict(X) + self.cate = pseudo_outcome_model.predict(X) - cate = (treated * (y - m1) / g + m1 - - ((1 - treated) * (y - m0) / (1 - g) + m0)) - - return cate def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + # Split data to two independent samples of equal size + ( + X0, X1, + y0, y1, + treated0, treated1 + ) = train_test_split(X, y, treated, stratify=treated, test_size=.5) + # Split data to treated and untreated subsets - X_t, y_t = X[treated == 1], y[treated == 1] - X_u, y_u = X[treated == 0], y[treated == 0] + X_t, y_t = X0[treated0 == 1], y0[treated0 == 1] + X_u, y_u = X0[treated0 == 0], y0[treated0 == 0] - treated_model, untreated_model, propensity_score_model = self.models.values() + treated_model, untreated_model, propensity_score_model, pseudo_outcome_model = self.models.values() # Estimate response functions _fit(treated_model, X_t, y_t, coords) @@ -428,9 +445,19 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): # Fit propensity score model _fit(propensity_score_model, X, treated, coords) + g = propensity_score_model.predict_proba(X1)[:, 1] + mu_0 = untreated_model.predict(X1) + mu_1 = treated_model.predict(X1) + mu_w = np.where(treated1==0, mu_0, mu_1) + + pseudo_outcome = ( + (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 + ) + + # Fit pseudo-outcome model + _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) + return self def predict_cate(self, X): - m1 = self.models["treated"].predict(X) - m0 = self.models["untreated"].predict(X) - return m1 - m0 + return self.models["pseudo_outcome"].predict(X) \ No newline at end of file From d62eb181b5bdb667831452d7852723da06941ec3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 8 Mar 2023 17:12:03 +0100 Subject: [PATCH 27/57] minor changes --- .../meta_learners_synthetic_data.ipynb | 425 ++++-------------- 1 file changed, 97 insertions(+), 328 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index 0c73135e..d77959cc 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -15,17 +15,13 @@ "source": [ "## Introduction\n", "\n", - "Meta-learners are a class of models used for estimating the effectiveness of a certain treatement and they are used when the effectiveness of a treatement is assumed to be *heterogeneous*, that is the effect of the treatement might vary across individuals. In particular, they estimate the treatement effectiveness by decomposing the problem to several subregressions. The models used for these subregressions will be called *base learners*. Some of the most popular base learners are tree based ensembles, in particular Random Forest and Bayesian Additive Regression Trees (BART) due to their flexibility, however any machine learning algorithm suitable for regression problems can be used.\n", + "Meta-learners are a class of models used for estimating *heterogeneous causal effect* of a certain treatement. Here by heterogeneity, we mean that the effect of the treatement may vary across individuals. They approach this problem by decomposing it to several subregressions. The models used for these subregressions are called *base learners*. Some of the most popular base learners are tree based ensembles, in particular Random Forest and Bayesian Additive Regression Trees (BART) due to their flexibility, however any machine learning algorithm suitable for regression problems (like generalized linear regressions, gaussian processes, etc.) can be used.\n", "\n", - "Following [1], we will denote by $Y$ an outcome vector, by $X_i \\in \\mathbb{R}^d$ a feature vector containing potential confounders for the $i$th unit, by $W_i \\in \\{0, 1\\}$ the treatment assignment indicator and by $Y_i (0) \\in \\mathbb{R}$ and $Y_i(1) \\in \\mathbb{R}$ the potential outcome of the $i$th unit when assigned to the control and the treatement group respectively. With this notation\n", - "\n", - "$$ Y = W Y(1) + (1 - W) Y(0). $$\n", + "We will denote by $Y$ a $d$-dimensional outcome vector, by $X \\in \\mathbb{R}^{d \\times n}$ a feature matrix containing potential confounders, by $W \\in \\{0, 1\\}^d$ the treatment assignment indicator and by $Y(0) \\in \\mathbb{R}^d$ and $Y(1) \\in \\mathbb{R}^d$ the potential outcome of corresponding units when assigned to the control and the treatement group respectively. Note that in general, we only observe one of the potential outcomes, furthermore\n", + "$$ Y = W Y(1) + (1 - W) Y(0).$$\n", "\n", "Our task is to estimate the *conditional average treatement effect (CATE)*, defined as \n", - "$$\\tau(x) = \\mathbb{E}\\left[Y(1) - Y(0) \\mid X=x\\right].$$\n", - "\n", - "The goal of this notebook is to introduce several meta-learners and compare their performance estimating CATE. The evaluation of an estimation $\\hat{\\tau}$ of $\\tau$ will be based on *expected mean squared error* defined as \n", - "$$\\operatorname{EMSE}(\\tau) = \\mathbb{E}\\left[\\left(\\tau(X) - \\hat{\\tau}(X)\\right)^2\\right].$$" + "$$\\tau(x) = \\mathbb{E}\\left[Y(1) - Y(0) \\mid X=x\\right].$$" ] }, { @@ -33,17 +29,18 @@ "id": "ed73c7c9-7df5-4912-80f4-c5e69c916c22", "metadata": {}, "source": [ - "## The fundamental problem of causal inference\n", + "## Data generation\n", "\n", - "The fact that only one of the potential outcomes ever materializes, means that CATE is never directly observed, thus on real world data, EMSE cannot be computed. This is often refered to as *the fundamental problem of causal inference*. To get around this problem, in this notebook we will deal with synthetic data with data generating process of the form\n", - "$$y = 10\\sigma(\\alpha X) + W \\sigma(\\beta X) + \\varepsilon,$$\n", - "where $\\varepsilon_i \\sim N(0, 1)$ and $W_i \\sim \\operatorname{Bernoulli}\\left(\\frac14\\right)$ are i.i.d. and $$\\sigma(x) = \\frac{e^x}{1 + e^x}$$ is the sigmoid function (applied coordinatewise).\n", - "Here, CATE can be expressed as $\\tau(X) = \\sigma(\\beta X)$." + "In this notebook we will deal with synthetic data, so that we can quantify exactly the mean squared error of our CATE estimations. The data generating is of the form\n", + "$$y = \\operatorname{sinc}(\\alpha X) + W \\sigma(\\beta X) + \\varepsilon,$$\n", + "where $\\varepsilon_i \\sim N(0, 1)$ and $W_i \\sim \\operatorname{Bernoulli}\\left(\\frac14\\right)$ are i.i.d., $$\\sigma(x) = \\frac{e^x}{1 + e^x}, \\quad \\text{and} \\quad \\operatorname{sinc}(x) = \\frac{\\sin(x)}{x}$$ are applied coordinate-wise.\n", + "\n", + "Here, CATE can be expressed as $$\\tau(X) = \\sigma(\\beta X).$$" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "id": "c543b717-acb7-4c7f-aa77-975833dbf699", "metadata": { "tags": [] @@ -55,12 +52,26 @@ "from causalpy.skl_meta_learners import SLearner, TLearner, XLearner, DRLearner\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.ensemble import HistGradientBoostingRegressor\n", + "from sklearn.dummy import DummyClassifier\n", + "from xgboost import XGBClassifier\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", "\n", "np.random.seed(42)\n", "\n", "def sigmoid(x):\n", " return np.exp(x) / (1 + np.exp(x))\n", "\n", + "def sinc(x):\n", + " return np.where(x==0, 1, np.sin(x) / x)\n", + "\n", + "def summarize_learner(learner):\n", + " learner.summary(n_iter=100)\n", + " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", + " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")\n", + "\n", + "\n", "def create_synthetic_data(*shape):\n", " X = np.random.rand(*shape)\n", "\n", @@ -72,14 +83,14 @@ "\n", " noise = np.random.rand(shape[0])\n", "\n", - " y = 10 * sigmoid(X @ α) + treatment_effect * treatment + noise\n", + " y = sinc(X @ α) + treatment_effect * treatment + noise\n", "\n", " X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(shape[1])])\n", " y = pd.Series(y, name='target')\n", " treated = pd.Series(treatment, name='treatment')\n", " return X, y, treated, treatment_effect\n", "\n", - "X, y, treated, treatment_effect = create_synthetic_data(1000, 10)" + "X, y, treated, treatment_effect = create_synthetic_data(500, 20)" ] }, { @@ -91,7 +102,7 @@ "source": [ "## S-learner\n", "\n", - "The simplest meta-learner is the *S-learner*, which treats the treatement assignment indicator as a feature and estimates \n", + "The simplest meta-learner is the *S-learner*, which predicts the potential outcome vectors by treating the treatement assignment indicator as a feature and estimates\n", "\n", "$$\\mu(x, w) := \\mathbb{E}[Y \\mid X=x, W=w]$$\n", "\n", @@ -104,34 +115,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "454322d0-6dbc-4771-bb7a-344afa30e793", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of observations: 1000\n", - "Number of treated observations: 261\n", - "Average treatement effect (ATE): 0.8825372188727141\n", - "95% Confidence interval for ATE: (0.5336478252676806, 1.3368338911656805)\n", - "Estimated bias: -0.005333370586446671\n", - "Actual average treatement effect: 0.8686759437628603\n", - "EMSE: 0.0489448259669642\n", - "CPU times: total: 8min 50s\n", - "Wall time: 1min 11s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "# Utility for printing some statistics.\n", - "def summarize_learner(learner):\n", - " learner.summary(n_iter=100)\n", - " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", - " print(f\"EMSE: {mean_squared_error(treatment_effect, learner.cate)}\")\n", - "\n", "slearner = SLearner(\n", " X=X,\n", " y=y,\n", @@ -142,48 +132,6 @@ "summarize_learner(slearner)" ] }, - { - "cell_type": "markdown", - "id": "d552d0e9-5d6c-4d4b-bde8-80c680a51e62", - "metadata": {}, - "source": [ - "**TODO: Do some plotting. As of now it plots the distribution of the CATE estimates. While that is not totally uninteresting, it is not good either.**\n", - "\n", - "More ideas: \n", - "- feature to estimated CATE scatter plot?\n", - "- several SHAP related things?\n", - "- ...?" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "059fd652-3fa9-42d3-b639-a48d96263314", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "slearner.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "3fe391b5-d8df-438f-ace2-0406d19e5813", - "metadata": {}, - "source": [ - "Eventough, the S-learner performs quite well in our synthetic example, it has some limitations. Most importantly, since the treatement indicator is included in the regression as an ordinary variable, it's effect may get lost, especially if the base learner is a tree based ensemble." - ] - }, { "cell_type": "markdown", "id": "c3d35f7f-6526-4b7d-ac36-2365fc08cb5d", @@ -197,37 +145,21 @@ "id": "54a8971c-5eef-4423-893e-9277e8a40ac1", "metadata": {}, "source": [ - "The *T-learner* solves the issue mentioned above by predicting $Y(0)$ and $Y(1)$ in two different regressions. Namely, we estimate\n", + "The *T-learner* is a closely related meta-learner. Here, instead of including the treatment assignment indicator as a feature, we estimate the potential outcomes in two different regressions. Namely, we estimate\n", "$$\\mu_{\\operatorname{treated}}(x) := \\mathbb{E}[Y(1) \\mid X=x], \\quad \\text{and} \\quad \\mu_{\\operatorname{untreated}}(x) := \\mathbb{E}[Y(0) \\mid X=x]$$\n", "separately and define estimate the cate as\n", "$$\\hat{\\tau}_T(x) = \\hat{\\mu}_{\\operatorname{treated}}(x) - \\hat{\\mu}_{\\operatorname{untreated}}(x),$$\n", "where $\\hat{\\mu}_{\\operatorname{treated}}$ and $\\hat{\\mu}_{\\operatorname{untreated}}$ are the estimated functions. \n", "\n", - "Again, we will choose *both* models to be `HistGradientBoostingRegressor`." + "If we choose our base-learners to be a forest based ensemble, in the case of the S-learner, during the fitting process splits on the treatement assignment indicator can happen *anywhere* in the trees. In the fitting process of the T-learner however, we essentially force a split at the first level. This difference means that the S-learner is more flexible, but especially when using regularization, it is prone to disregard the effect of the treatment. On the other hand the T-learner uses two separate models that do not share information between each other, which may lead to overfitting if one of the treatement groups is small." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "2a24a76e-398a-4629-9d49-7fadecb2a2c7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of observations: 1000\n", - "Number of treated observations: 261\n", - "Average treatement effect (ATE): 0.8778680189940496\n", - "95% Confidence interval for ATE: (0.44254662378948145, 1.4724386927005046)\n", - "Estimated bias: 0.0075568485724106125\n", - "Actual average treatement effect: 0.8686759437628603\n", - "EMSE: 0.09250244262195867\n", - "CPU times: total: 11min 16s\n", - "Wall time: 1min 32s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "tlearner = TLearner(\n", @@ -237,7 +169,6 @@ " model=HistGradientBoostingRegressor()\n", ")\n", "\n", - "\n", "summarize_learner(tlearner)" ] }, @@ -247,127 +178,53 @@ "metadata": {}, "source": [ "# X-learner\n", - "The X-learner performs better than other CATE estimators when\n", - "- one of the treatement groups is significantly larger than the other one, \n", - "- when the CATE function has structural properties such as sparsity or smoothness, it can provably adapt [1].\n", - "\n", - "Very similarly, to the T-learner, the X-learner first estimates $\\mu_{\\operatorname{treated}}$ and $\\mu_{\\operatorname{untreated}}$. Next, it computes \n", + "Similarly, to the T-learner, the X-learner first estimates $\\mu_{\\operatorname{treated}}$ and $\\mu_{\\operatorname{untreated}}$. Next, it computes \n", "\n", "$$D(1) := Y(1) - \\hat{\\mu}_{\\operatorname{untreated}}(X(1)), \\quad \\text{and} \\quad D(0) = \\hat{\\mu}_{\\operatorname{treated}}(X(0)) - Y(0).$$\n", "\n", - "Note that if $\\hat{\\mu}_{\\operatorname{untreated}} = \\mu_{\\operatorname{untreated}}$ and $\\hat{\\mu}_{\\operatorname{treated}} = \\mu_{\\operatorname{treated}}$, then $\\tau(x) = \\mathbb{E}[D(1) | X=x] = \\mathbb{E}[D(0) | X=x]$, so CATE can be estimated by regressing onto $D(0)$ or $D(1)$. Let us denote these estimations by $\\tau_0$ and $\\tau_1$, respectively. \n", + "Note that if $\\hat{\\mu}_{\\operatorname{untreated}} = \\mu_{\\operatorname{untreated}}$ and $\\hat{\\mu}_{\\operatorname{treated}} = \\mu_{\\operatorname{treated}}$, then $\\tau(x) = \\mathbb{E}[D(1) | X=x] = \\mathbb{E}[D(0) | X=x]$, so CATE may be estimated by regressing on $D(0)$ or $D(1)$. Let us denote the estimations obtained this way by $\\tau_0$ and $\\tau_1$, respectively. \n", "\n", "Finally, the X-learner computes a weighted average of the two CATE estimates, that is \n", "$$\\hat{\\tau}_X(x) = g(x)\\hat{\\tau}_0 + (1 - g(x))\\hat{\\tau_1}(x),$$\n", - "for some weight function $g: \\mathbb{R}^d \\rightarrow [0, 1]$." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "250bd516-b1ad-452f-b26c-e0eafc3747f8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of observations: 1000\n", - "Number of treated observations: 261\n", - "Average treatement effect (ATE): 0.8656013308211011\n", - "95% Confidence interval for ATE: (0.5670698664248546, 1.2446227193486425)\n", - "Estimated bias: -0.0031460678827127334\n", - "Actual average treatement effect: 0.8686759437628603\n", - "EMSE: 0.03875185597434108\n", - "CPU times: total: 22min 8s\n", - "Wall time: 3min 2s\n" - ] - } - ], - "source": [ - "%%time\n", - "xlearner = XLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", + "for some weight function $g: \\mathbb{R}^d \\rightarrow [0, 1]$.\n", "\n", - "summarize_learner(xlearner)" - ] - }, - { - "cell_type": "markdown", - "id": "2668f04a-6a8a-495d-b14c-6a61d15ff795", - "metadata": {}, - "source": [ - "In CausalPy, we obtain $g$ as an estimation of the so-called *propensity score*, that is the conditional probability of a unit receiving treatment given $X$. By default, we use logistic regression to obtain this estimation. However, in case one of the treatement groups is much larger than the other one, it makes sense to set $g \\equiv 0$ or $g \\equiv 1$. This can be done the following way. " + "In CausalPy, we obtain $g$ as an estimation of the so-called *propensity score*, that is the conditional probability of a unit receiving treatment given $X$. By default, we use logistic regression to obtain this estimation. However, in case one of the treatement groups is small, it makes sense to set $g \\equiv 0$ or $g \\equiv 1$." ] }, { "cell_type": "code", - "execution_count": 37, - "id": "aaab018b-c95e-4c29-9163-410e39cd34de", + "execution_count": null, + "id": "250bd516-b1ad-452f-b26c-e0eafc3747f8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of observations: 1000\n", - "Number of treated observations: 261\n", - "Average treatement effect (ATE): 0.8285314927602266\n", - "95% Confidence interval for ATE: (0.5295382157583198, 1.275838462466607)\n", - "Estimated bias: -0.010891457791703623\n", - "Actual average treatement effect: 0.8686759437628603\n", - "EMSE: 0.03875185597434108\n", - "CPU times: total: 22min 37s\n", - "Wall time: 3min 2s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", - "from sklearn.dummy import DummyClassifier\n", - "\n", - "xlearner_mf = XLearner(\n", + "# Using logistic regression based propensity score\n", + "xlearner = XLearner(\n", " X=X,\n", " y=y,\n", " treated=treated,\n", - " model=HistGradientBoostingRegressor(),\n", - " propensity_score_model=DummyClassifier(strategy=\"most_frequent\")\n", + " model=HistGradientBoostingRegressor()\n", ")\n", "\n", - "summarize_learner(xlearner_mf)" + "summarize_learner(xlearner)" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "e6ae334d-c413-43ff-98b3-02e6071c4d7d", + "execution_count": null, + "id": "aaab018b-c95e-4c29-9163-410e39cd34de", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of observations: 1000\n", - "Number of treated observations: 261\n", - "Average treatement effect (ATE): 0.8257963466324518\n", - "95% Confidence interval for ATE: (0.6015347065733898, 1.2304421938761037)\n", - "Estimated bias: -0.005635485886235879\n", - "Actual average treatement effect: 0.8686759437628603\n", - "EMSE: 0.03875185584711515\n", - "CPU times: total: 29min 2s\n", - "Wall time: 4min 7s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", - "from xgboost import XGBClassifier\n", - "\n", + "# Using constant g\n", "xlearner_mf = XLearner(\n", " X=X,\n", " y=y,\n", " treated=treated,\n", " model=HistGradientBoostingRegressor(),\n", - " propensity_score_model=XGBClassifier()\n", + " propensity_score_model=DummyClassifier(strategy=\"most_frequent\")\n", ")\n", "\n", "summarize_learner(xlearner_mf)" @@ -379,179 +236,91 @@ "metadata": {}, "source": [ "# DR-learner\n", - "The DR-learner ..." + "The *doubly robust learner*, also known as the *DR-learner*, is the last CATE estimator that we are going to look at." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "687aa584-b8af-4863-a7cf-570e86080057", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of observations: 1000\n", - "Number of treated observations: 261\n", - "Average treatement effect (ATE): 0.8617951727930917\n", - "95% Confidence interval for ATE: (0.4540090400090874, 1.4731133930096414)\n", - "Estimated bias: 0.001972318343075466\n", - "Actual average treatement effect: 0.8686759437628603\n", - "EMSE: 0.1859824363704813\n", - "CPU times: total: 11min 25s\n", - "Wall time: 1min 32s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", - "drlearner = DRLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", + "drlearner = DRLearner(\n", + " X=X,\n", + " y=y,\n", + " treated=treated,\n", + " model=HistGradientBoostingRegressor(),\n", + " propensity_score_model=XGBClassifier()\n", + ")\n", "\n", "summarize_learner(drlearner)" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "648ef110-5a67-4eca-8ff0-0ddb0308c866", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor, AdaBoostRegressor\n", - "from lightgbm import LGBMRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a2cc2c39-2690-4943-90a9-b90fd2a60972", - "metadata": {}, - "outputs": [], - "source": [ - "def compute_emse(learner, m):\n", - " l = learner(X_train, y_train, treated_train, m)\n", - " return mean_squared_error(effect_train, l.cate)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "3552a561-8051-4a51-832a-5e088a106689", - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, "outputs": [], "source": [ + "from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor, AdaBoostRegressor\n", + "from lightgbm import LGBMRegressor\n", + "from sklearn.svm import SVR\n", + "from xgboost import XGBRegressor\n", + "\n", + "def compute_mse(learner, m):\n", + " l = learner(X, y, treated, m)\n", + " return mean_squared_error(treatment_effect, l.cate)\n", + "\n", "models = [\n", " (\"Random forest\", RandomForestRegressor()),\n", " (\"Hist gradient boosting\", HistGradientBoostingRegressor()),\n", " (\"AdaBoost\", AdaBoostRegressor()),\n", " (\"XGBoost\", XGBRegressor()),\n", - " (\"LightGBM\", LGBMRegressor())\n", + " (\"LightGBM\", LGBMRegressor()),\n", + " (\"SVR\", SVR())\n", + "]\n", + "\n", + "learners = [\n", + " (\"S-learner\", SLearner),\n", + " (\"T-learner\", TLearner),\n", + " (\"X-learner\", XLearner),\n", + " (\"DR-learner\", DRLearner)\n", "]\n", "bench = pd.DataFrame({\n", " m_name: {\n", - " 'SLearner': compute_emse( SLearner, m),\n", - " 'TLearner': compute_emse( TLearner, m),\n", - " 'XLearner': compute_emse( XLearner, m),\n", - " 'DRLearner': compute_emse(DRLearner, m)\n", - " } for m_name, m in models\n", + " learner_name: compute_emse(learner, m) for learner_name, learner in learners\n", + " } for m_name, m in models\n", "})" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "7eed6094-c940-4b5b-8294-bd2ffeab2eac", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Random forestHist gradient boostingAdaBoostXGBoostLightGBM
SLearner0.0432660.0287200.0054360.0485270.030729
TLearner0.0453080.0559680.0115860.1090950.052863
XLearner0.0275510.0429980.0088280.0692270.041362
DRLearner0.1649980.1798210.2793970.1096300.184410
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" - ], - "text/plain": [ - " Random forest Hist gradient boosting AdaBoost XGBoost LightGBM\n", - "SLearner 0.043266 0.028720 0.005436 0.048527 0.030729\n", - "TLearner 0.045308 0.055968 0.011586 0.109095 0.052863\n", - "XLearner 0.027551 0.042998 0.008828 0.069227 0.041362\n", - "DRLearner 0.164998 0.179821 0.279397 0.109630 0.184410" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "bench" + "fig, ax = plt.subplots(figsize=(10,10))\n", + "sns.heatmap(bench, annot=True, ax=ax)" ] }, { - "cell_type": "markdown", - "id": "093a6965-0ef3-4d7b-93d8-138b7602517a", + "cell_type": "raw", + "id": "0df81690-aba8-4f1c-9e2a-feb016664fca", "metadata": {}, "source": [ - "# TODO: EMSE as a function of population size" + "Bibliography:\n", + "[1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu.\n", + " Metalearners for estimating heterogeneous treatment effects using machine learning.\n", + " Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165." ] } ], From 95e010ec602105eb4765d6aee51a3170312ceeaa Mon Sep 17 00:00:00 2001 From: "mate.kadlicsko" Date: Thu, 9 Mar 2023 14:57:38 +0100 Subject: [PATCH 28/57] implemented cross_fitting option for DR-learner --- causalpy/skl_meta_learners.py | 78 +++++++++++++++++++++++------------ 1 file changed, 52 insertions(+), 26 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index fc8b630f..224e5bb1 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -416,12 +416,18 @@ def __init__( "propensity": propensity_score_model, "pseudo_outcome": pseudo_outcome_model } - + + cross_fitted_models = {} + if self.cross_fitting: + cross_fitted_models = {key: deepcopy(value) for key, value in self.models.items()} + + self.cross_fitted_models = cross_fitted_models + COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} self.fit(X, y, treated, coords=COORDS) # Estimate CATE - self.cate = pseudo_outcome_model.predict(X) + self.cate = self.predict_cate(X) def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): @@ -431,33 +437,53 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): y0, y1, treated0, treated1 ) = train_test_split(X, y, treated, stratify=treated, test_size=.5) - - # Split data to treated and untreated subsets - X_t, y_t = X0[treated0 == 1], y0[treated0 == 1] - X_u, y_u = X0[treated0 == 0], y0[treated0 == 0] - + treated_model, untreated_model, propensity_score_model, pseudo_outcome_model = self.models.values() - - # Estimate response functions - _fit(treated_model, X_t, y_t, coords) - _fit(untreated_model, X_u, y_u, coords) - - # Fit propensity score model - _fit(propensity_score_model, X, treated, coords) - - g = propensity_score_model.predict_proba(X1)[:, 1] - mu_0 = untreated_model.predict(X1) - mu_1 = treated_model.predict(X1) - mu_w = np.where(treated1==0, mu_0, mu_1) - - pseudo_outcome = ( - (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 - ) - # Fit pseudo-outcome model - _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) + # Second iteration is the cross-fitting step. + second_iteration = False + for i in range(2): + # Split data to treated and untreated subsets + X_t, y_t = X0[treated0 == 1], y0[treated0 == 1] + X_u, y_u = X0[treated0 == 0], y0[treated0 == 0] + + # Estimate response functions + _fit(treated_model, X_t, y_t, coords) + _fit(untreated_model, X_u, y_u, coords) + + # Fit propensity score model + _fit(propensity_score_model, X, treated, coords) + + g = propensity_score_model.predict_proba(X1)[:, 1] + mu_0 = untreated_model.predict(X1) + mu_1 = treated_model.predict(X1) + mu_w = np.where(treated1==0, mu_0, mu_1) + + pseudo_outcome = ( + (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 + ) + + # Fit pseudo-outcome model + _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) + + if self.cross_fitting and not second_iteration: + # Swap data and estimators + (X0, X1) = (X1, X0) + (y0, y1) = (y1, y0) + (treated0, treated1) = (treated1, treated0) + + treated_model, untreated_model, propensity_score_model, pseudo_outcome_model = self.cross_fitted_models.values() + second_iteration = True + else: + return self return self def predict_cate(self, X): - return self.models["pseudo_outcome"].predict(X) \ No newline at end of file + pred1 = self.models["pseudo_outcome"].predict(X) + + if self.cross_fitting: + pred2 = self.cross_fitted_models["pseudo_outcome"].predict(X) + return (pred1 + pred2) / 2 + + return pred1 \ No newline at end of file From 3e1182dfb4c45adec999f8ada0bb483f873dbdf7 Mon Sep 17 00:00:00 2001 From: "mate.kadlicsko" Date: Thu, 9 Mar 2023 16:54:06 +0100 Subject: [PATCH 29/57] wrote subsection on DR-learner --- .../meta_learners_synthetic_data.ipynb | 243 +++++++++++++++--- 1 file changed, 211 insertions(+), 32 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index d77959cc..c12b0061 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -40,12 +40,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "id": "c543b717-acb7-4c7f-aa77-975833dbf699", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -66,12 +74,6 @@ "def sinc(x):\n", " return np.where(x==0, 1, np.sin(x) / x)\n", "\n", - "def summarize_learner(learner):\n", - " learner.summary(n_iter=100)\n", - " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", - " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")\n", - "\n", - "\n", "def create_synthetic_data(*shape):\n", " X = np.random.rand(*shape)\n", "\n", @@ -93,6 +95,35 @@ "X, y, treated, treatment_effect = create_synthetic_data(500, 20)" ] }, + { + "cell_type": "markdown", + "id": "19940b36-b376-4e2c-9899-91a5162319c8", + "metadata": {}, + "source": [ + "## Summary\n", + "The summary method of a scikit-learn based meta-learner contains\n", + "* Number of observations,\n", + "* number of treated observations,\n", + "* average treatement effect (ATE),\n", + "* 95% Confidence interval for ATE,\n", + "* estimated bias.\n", + "\n", + "Confidence intervals and bias are calculated when summary is called via bootstrapping. This means that the runtime of each learner in these examples will be exaggerated. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8dc5e4d6-c632-4ca2-85e6-2d9a7aae892f", + "metadata": {}, + "outputs": [], + "source": [ + "def summarize_learner(learner):\n", + " learner.summary(n_iter=100)\n", + " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", + " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")" + ] + }, { "cell_type": "markdown", "id": "9cf837e1-d93c-4fc5-bc52-96296db82e51", @@ -115,10 +146,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "454322d0-6dbc-4771-bb7a-344afa30e793", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 126\n", + "Average treatement effect (ATE): 1.0158653426134698\n", + "95% Confidence interval for ATE: (0.7168756519845457, 1.4142990694909983)\n", + "Estimated bias: -0.0032205093538485788\n", + "Actual average treatement effect: 0.9935910373038435\n", + "MSE: 0.04298096898757659\n", + "CPU times: total: 6min 23s\n", + "Wall time: 55.9 s\n" + ] + } + ], "source": [ "%%time\n", "# Utility for printing some statistics.\n", @@ -156,10 +203,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "2a24a76e-398a-4629-9d49-7fadecb2a2c7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 126\n", + "Average treatement effect (ATE): 1.0602041004116796\n", + "95% Confidence interval for ATE: (0.6678309683577215, 1.5429661284355667)\n", + "Estimated bias: 0.009071047889501997\n", + "Actual average treatement effect: 0.9935910373038435\n", + "MSE: 0.08202638716051415\n", + "CPU times: total: 6min 32s\n", + "Wall time: 55.4 s\n" + ] + } + ], "source": [ "%%time\n", "tlearner = TLearner(\n", @@ -193,10 +256,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "250bd516-b1ad-452f-b26c-e0eafc3747f8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 126\n", + "Average treatement effect (ATE): 1.0241946147950118\n", + "95% Confidence interval for ATE: (0.7780033378922033, 1.3792194001196363)\n", + "Estimated bias: -0.0030826969031921064\n", + "Actual average treatement effect: 0.9935910373038435\n", + "MSE: 0.04195048793085756\n", + "CPU times: total: 13min 10s\n", + "Wall time: 1min 52s\n" + ] + } + ], "source": [ "%%time\n", "# Using logistic regression based propensity score\n", @@ -212,10 +291,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "aaab018b-c95e-4c29-9163-410e39cd34de", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 126\n", + "Average treatement effect (ATE): 1.036517292248574\n", + "95% Confidence interval for ATE: (0.7821662021543369, 1.3970869383992666)\n", + "Estimated bias: 0.0016462894459864125\n", + "Actual average treatement effect: 0.9935910373038435\n", + "MSE: 0.04195048793085756\n", + "CPU times: total: 13min 5s\n", + "Wall time: 1min 48s\n" + ] + } + ], "source": [ "%%time\n", "# Using constant g\n", @@ -230,21 +325,51 @@ "summarize_learner(xlearner_mf)" ] }, + { + "cell_type": "markdown", + "id": "adf8263c-1ceb-4ee8-9302-69961892214f", + "metadata": {}, + "source": [ + "For a deeper dive on the aforementioned three estimators I suggest reading [1] or for a lighter overview in video format, check out [Causal Effects via Regression by Shawhin Talebi](https://youtu.be/O72uByJlnMw)." + ] + }, { "cell_type": "markdown", "id": "6a499cc5-2fcf-4016-978b-c3c28c861d5a", "metadata": {}, "source": [ "# DR-learner\n", - "The *doubly robust learner*, also known as the *DR-learner*, is the last CATE estimator that we are going to look at." + "\n", + "The *doubly-robust learner*, also known as the *DR-learner*, takes its name from the property that it is unbiased if either the propensity score estimator or the outcome regressions are correctly specified.\n", + "\n", + "The DR-learner first splits the set of observations $S = (Y, X, W)$ randomly into two parts $S_i = (Y_i, X_i, W_i), i=1,2$ of equally many observations, finds the estimates $\\hat{\\mu}_{\\operatorname{treated}}$, $\\hat{\\mu}_{\\operatorname{untreated}}$ and $g$ on $S_1$. Then it constructs \n", + "\n", + "$$\\varphi(X) = \\frac{W - g(X)}{g(X)(1 - g(X))}(Y - \\hat{\\mu}_{W}(X)) + \\hat{\\mu}_{\\operatorname{treated}} - \\hat{\\mu}_{\\operatorname{untreated}}$$\n", + "called the *pseudo-outcome*, where $\\hat{\\mu}_W(X) = (1 - W)\\hat{\\mu}_{\\operatorname{untreated}} + W\\hat{\\mu}_{\\operatorname{treated}}$. Finally, regressing $\\varphi(X_2)$ on $X_2$ yields the CATE estimator $\\hat{\\tau}_{DR}$. Optionally, one may perform a *cross-fitting step* by swapping the roles of $S_1$ and $S_2$ obtaining a second estimator. Averaging the two estimators usually results in a better one." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "687aa584-b8af-4863-a7cf-570e86080057", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 126\n", + "Average treatement effect (ATE): 1.043603596223281\n", + "95% Confidence interval for ATE: (0.5722356606341764, 1.791826637224789)\n", + "Estimated bias: 0.06476430795642821\n", + "Actual average treatement effect: 0.9935910373038435\n", + "MSE: 0.26413051181872693\n", + "CPU times: total: 7min 7s\n", + "Wall time: 58.8 s\n" + ] + } + ], "source": [ "%%time\n", "drlearner = DRLearner(\n", @@ -252,7 +377,7 @@ " y=y,\n", " treated=treated,\n", " model=HistGradientBoostingRegressor(),\n", - " propensity_score_model=XGBClassifier()\n", + " propensity_score_model=DummyClassifier()\n", ")\n", "\n", "summarize_learner(drlearner)" @@ -260,12 +385,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "id": "ea54609b-9f4f-4282-b60c-498de1af106b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations: 500\n", + "Number of treated observations: 126\n", + "Average treatement effect (ATE): 1.0124610535982221\n", + "95% Confidence interval for ATE: (0.5464324003175869, 1.6467470784122396)\n", + "Estimated bias: -0.012888295172939901\n", + "Actual average treatement effect: 0.9935910373038435\n", + "MSE: 0.11390230987800598\n", + "CPU times: total: 14min 6s\n", + "Wall time: 1min 56s\n" + ] + } + ], + "source": [ + "%%time\n", + "drlearner = DRLearner(\n", + " X=X,\n", + " y=y,\n", + " treated=treated,\n", + " model=HistGradientBoostingRegressor(),\n", + " propensity_score_model=DummyClassifier(),\n", + " cross_fitting=True\n", + ")\n", + "\n", + "summarize_learner(drlearner)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "3552a561-8051-4a51-832a-5e088a106689", "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, "outputs": [], @@ -284,7 +442,6 @@ " (\"Hist gradient boosting\", HistGradientBoostingRegressor()),\n", " (\"AdaBoost\", AdaBoostRegressor()),\n", " (\"XGBoost\", XGBRegressor()),\n", - " (\"LightGBM\", LGBMRegressor()),\n", " (\"SVR\", SVR())\n", "]\n", "\n", @@ -292,21 +449,43 @@ " (\"S-learner\", SLearner),\n", " (\"T-learner\", TLearner),\n", " (\"X-learner\", XLearner),\n", - " (\"DR-learner\", DRLearner)\n", + " (\"DR-learner\", DRLearner),\n", + " (\"Cross-fitted DR-learner\", lambda *args: DRLearner(*args, cross_fitting=True))\n", "]\n", "bench = pd.DataFrame({\n", " m_name: {\n", - " learner_name: compute_emse(learner, m) for learner_name, learner in learners\n", + " learner_name: compute_mse(learner, m) for learner_name, learner in learners\n", " } for m_name, m in models\n", "})" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "7eed6094-c940-4b5b-8294-bd2ffeab2eac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10,10))\n", "sns.heatmap(bench, annot=True, ax=ax)" @@ -318,9 +497,9 @@ "metadata": {}, "source": [ "Bibliography:\n", - "[1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu.\n", - " Metalearners for estimating heterogeneous treatment effects using machine learning.\n", - " Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165." + "\n", + "[1] Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165.\n", + "[2] Kennedy, E. H. Optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint (2020) arXiv:2004.14497." ] } ], From 806cd0f13d1efdfc704a81ae91bbaf1898d7b62f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Fri, 10 Mar 2023 23:22:03 +0100 Subject: [PATCH 30/57] added docstring + some small changes suggested by @juanitorduz --- causalpy/skl_meta_learners.py | 326 ++++++++++++++++++++++++++-------- 1 file changed, 247 insertions(+), 79 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 224e5bb1..00d7c6ec 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -2,9 +2,11 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +from sklearn.base import ClassifierMixin, RegressorMixin from sklearn.linear_model import LogisticRegression -from sklearn.utils import check_consistent_length from sklearn.model_selection import train_test_split +from sklearn.utils import check_consistent_length +from typing import Any, Dict from causalpy.utils import _is_variable_dummy_coded, _fit @@ -18,16 +20,26 @@ def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: if not _is_variable_dummy_coded(treated): raise ValueError("Treatment variable is not dummy coded.") + # Check whether input data share the same index + if not ((X.index == y.index) & (y.index == treated.index)).all(): + raise ValueError("Indices of input data do not coincide.") + self.cate = None self.treated = treated self.X = X self.y = y self.models = {} + self.labels = X.columns + self.index = X.index def predict_cate(self, X: pd.DataFrame) -> np.array: """ Predict out-of-sample conditional average treatment effect on given input X. For in-sample treatement effect self.cate should be used. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). """ raise NotImplementedError() @@ -35,18 +47,34 @@ def predict_ate(self, X: pd.DataFrame) -> np.float64: """ Predict out-of-sample average treatment effect on given input X. For in-sample treatement effect self.ate() should be used. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). """ return self.predict_cate(X).mean() - def ate(self): + def ate(self) -> np.float64: "Returns in-sample average treatement effect." return self.cate.mean() - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): - "Fits model." + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): + """Fits model. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + coords : Dict[str, Any]. + Dictionary containing the keys coeffs and obs_indx. + """ raise NotImplementedError() - def summary(self): + def summary(self) -> None: "Prints summary." print(f"Number of observations: {self.X.shape[0]}") print(f"Number of treated observations: {self.treated.sum()}") @@ -66,11 +94,24 @@ def bootstrap( y: pd.Series, treated: pd.Series, X: pd.DataFrame = None, - n_iter: int = 1000 + n_iter: int = 1000, ) -> np.array: """ Runs bootstrap n_iter times on a sample of size n_samples. Fits on (X_ins, y, treated), then predicts on X. + + Parameters + ---------- + X_ins : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + X : pandas.DataFrame of shape (_, n_features). + Data to predict on. + n_iter : int, default = 1000. + Number of bootstrap iterations to perform. """ if X is None: X = X_ins @@ -114,9 +155,25 @@ def ate_confidence_interval( treated: pd.Series, X: pd.DataFrame = None, q: float = .05, - n_iter: int = 1000 + n_iter: int = 1000, ) -> tuple: - "Estimates confidence intervals for ATE on X using bootstraping." + """Estimates confidence intervals for ATE on X using bootstraping. + + Parameters + ---------- + X_ins : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + X : pandas.DataFrame of shape (_, n_features). + Data to predict on. + q : float, default=.05. + Quantile to compute. Should be between in the interval [0, 1]. + n_iter : int, default = 1000. + Number of bootstrap iterations to perform. + """ cates = self.bootstrap(X_ins, y, treated, X, n_iter) ates = cates.mean(axis=0) return np.quantile(ates, q=q / 2), np.quantile(cates, q=1 - q / 2) @@ -128,9 +185,24 @@ def cate_confidence_interval( treated: pd.Series, X: pd.DataFrame = None, q: float = .05, - n_iter: int = 1000 + n_iter: int = 1000, ) -> np.array: - "Estimates confidence intervals for CATE on X using bootstraping." + """Estimates confidence intervals for CATE on X using bootstraping. + + Parameters + ---------- + X_ins : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + X : pandas.DataFrame of shape (_, n_features). + Data to predict on. + q : float, default=.05. + Quantile to compute. Should be between in the interval [0, 1]. + n_iter : int, default = 1000. + Number of bootstrap iterations to perform.""" cates = self.bootstrap(X_ins, y, treated, X, n_iter) conf_ints = np.append( np.quantile(cates, q / 2, axis=0).reshape(-1, 1), @@ -146,9 +218,24 @@ def bias( treated: pd.Series, X: pd.DataFrame = None, q: float = .05, - n_iter: int = 1000 - ): - "Calculates bootstrap estimate of bias of CATE estimator." + n_iter: int = 1000, + ) -> np.float64: + """Calculates bootstrap estimate of bias of CATE estimator. + + Parameters + ---------- + X_ins : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + X : pandas.DataFrame of shape (_, n_features). + Data to predict on. + q : float, default=.05. + Quantile to compute. Should be between in the interval [0, 1]. + n_iter : int, default = 1000. + Number of bootstrap iterations to perform.""" if X is None: X = X_ins @@ -157,14 +244,20 @@ def bias( return (bs_pred - pred).mean() - + def summary(self, n_iter: int = 1000) -> None: + """ + Prints summary. - def summary(self, n_iter=1000): + Parameters + ---------- + n_iter : int, default=1000. + Number of bootstrap iterations to perform. + """ # TODO: we run self.bootstrap twice independently. bias = self.bias(self.X, self.y, self.treated, self.X, n_iter=n_iter) conf_ints = self.ate_confidence_interval( self.X, self.y, self.treated, self.X, n_iter=n_iter - ) + ) print(f"Number of observations: {self.X.shape[0]}") print(f"Number of treated observations: {self.treated.sum()}") print(f"Average treatement effect (ATE): {self.predict_ate(self.X)}") @@ -181,23 +274,33 @@ class SLearner(SkMetaLearner): Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : sklearn.base.RegressorMixin. + Base learner. """ def __init__( - self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, model + self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, model: RegressorMixin ) -> None: super().__init__(X=X, y=y, treated=treated) self.models["model"] = model COORDS = { - "coeffs": list(X.columns) + ["treated"], - "obs_indx": np.arange(X.shape[0]) + "coeffs": list(self.labels) + ["treated"], + "obs_indx": self.index } self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): X_t = X.assign(treatment=treated) _fit(model=self.models["model"], X=X_t, y=y, coords=coords) return self @@ -217,7 +320,22 @@ class TLearner(SkMetaLearner): [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. - + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : sklearn.base.RegressorMixin. + If specified, it will be used both as treated and untreated model. Either model or + both of treated_model and untreated_model have to be specified. + treated_model: sklearn.base.RegressorMixin. + Model used for predicting target vector for treated values. + untreated_model: sklearn.base.RegressorMixin. + Model used for predicting target vector for untreated values. """ def __init__( @@ -225,9 +343,9 @@ def __init__( X: pd.DataFrame, y: pd.Series, treated: pd.Series, - model=None, - treated_model=None, - untreated_model=None + model: RegressorMixin = None, + treated_model: RegressorMixin = None, + untreated_model: RegressorMixin = None, ) -> None: super().__init__(X=X, y=y, treated=treated) @@ -248,12 +366,12 @@ def __init__( self.models = {"treated": treated_model, "untreated": untreated_model} - COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + COORDS = {"coeffs": self.labels, "obs_indx": self.index} self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): X_t, y_t = X[treated == 1], y[treated == 1] X_u, y_u = X[treated == 0], y[treated == 0] _fit(model=self.models["treated"], X=X_t, y=y_t, coords=coords) @@ -275,19 +393,42 @@ class XLearner(SkMetaLearner): Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : sklearn.base.RegressorMixin. + If specified, it will be used in all of the subregressions, except for the + propensity_score_model. Either model or all of treated_model, untreated_model, + treated_cate_estimator and untreated_cate_estimator have to be specified. + treated_model : sklearn.base.RegressorMixin. + Model used for predicting target vector for treated values. + untreated_model : sklearn.base.RegressorMixin. + Model used for predicting target vector for untreated values. + untreated_cate_estimator : sklearn.base.RegressorMixin + Model used for CATE estimation on untreated data. + treated_cate_estimator : sklearn.base.RegressorMixin + Model used for CATE estimation on treated data. + propensity_score_model : sklearn.base.ClassifierMixin, + default = sklearn.linear_model.LogisticRegression(). + Model used for propensity score estimation. """ def __init__( self, - X, - y, - treated, - model=None, - treated_model=None, - untreated_model=None, - treated_cate_estimator=None, - untreated_cate_estimator=None, - propensity_score_model=LogisticRegression(penalty=None) + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model: RegressorMixin = None, + treated_model: RegressorMixin = None, + untreated_model: RegressorMixin = None, + treated_cate_estimator: RegressorMixin = None, + untreated_cate_estimator: RegressorMixin = None, + propensity_score_model: ClassifierMixin = LogisticRegression(penalty=None), ): super().__init__(X=X, y=y, treated=treated) @@ -316,28 +457,26 @@ def __init__( "propensity": propensity_score_model } - COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + COORDS = {"coeffs": self.labels, "obs_indx": self.index} self.fit(X, y, treated, coords=COORDS) # Compute cate self.cate = self._compute_cate(X) - def _compute_cate(self, X): + def _compute_cate(self, X: pd.Series): "Computes cate for given input." cate_t = self.models["treated_cate"].predict(X) cate_u = self.models["treated_cate"].predict(X) g = self.models["propensity"].predict_proba(X)[:, 1] return g * cate_u + (1 - g) * cate_t - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): - ( - treated_model, - untreated_model, - treated_cate_estimator, - untreated_cate_estimator, - propensity_score_model - ) = self.models.values() + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): + treated_model = self.models["treated"] + untreated_model = self.models["untreated"] + treated_cate_estimator = self.models["treated_cate"] + untreated_cate_estimator = self.models["untreated_cate"] + propensity_score_model = self.models["propensity"] # Split data to treated and untreated subsets X_t, y_t = X[treated == 1], y[treated == 1] @@ -358,7 +497,7 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): _fit(propensity_score_model, X, treated, coords) return self - def predict_cate(self, X): + def predict_cate(self, X: pd.DataFrame) -> np.array: cate_estimate_treated = self.models["treated_cate"].predict(X) cate_estimate_untreated = self.models["untreated_cate"].predict(X) @@ -374,23 +513,46 @@ class DRLearner(SkMetaLearner): [1] Curth, Alicia, Mihaela van der Schaar. Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms. International Conference on Artificial Intelligence and Statistics, pp. 1810-1818 (2021). - + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : sklearn.base.RegressorMixin. + If specified, it will be used in all of the subregressions, except for the + propensity_score_model. Either model or all of treated_model, untreated_model, + treated_cate_estimator and untreated_cate_estimator have to be specified. + treated_model : sklearn.base.RegressorMixin. + Model used for predicting target vector for treated values. + untreated_model : sklearn.base.RegressorMixin. + Model used for predicting target vector for untreated values. + pseudo_outcome_model : sklearn.base.RegressorMixin + Model used for pseudo-outcome estimation. + propensity_score_model : sklearn.base.ClassifierMixin, + default = sklearn.linear_model.LogisticRegression(). + Model used for propensity score estimation. + cross_fitting : bool, default=False. + If True, performs a cross fitting step. """ def __init__( self, - X, - y, - treated, - model=None, - treated_model=None, - untreated_model=None, - pseudo_outcome_model=None, - propensity_score_model=LogisticRegression(penalty=None), - cross_fitting=False + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model: RegressorMixin = None, + treated_model: RegressorMixin = None, + untreated_model: RegressorMixin = None, + pseudo_outcome_model: RegressorMixin = None, + propensity_score_model: ClassifierMixin = LogisticRegression(penalty=None), + cross_fitting: bool = False, ): super().__init__(X=X, y=y, treated=treated) - + self.cross_fitting = cross_fitting if model is None and (untreated_model is None or treated_model is None): @@ -416,31 +578,34 @@ def __init__( "propensity": propensity_score_model, "pseudo_outcome": pseudo_outcome_model } - - cross_fitted_models = {} + + cross_fitted_models = {} if self.cross_fitting: - cross_fitted_models = {key: deepcopy(value) for key, value in self.models.items()} - + cross_fitted_models = {key: deepcopy(value) + for key, value in self.models.items()} + self.cross_fitted_models = cross_fitted_models - - COORDS = {"coeffs": X.columns, "obs_indx": np.arange(X.shape[0])} + + COORDS = {"coeffs": self.labels, "obs_indx": self.index} self.fit(X, y, treated, coords=COORDS) # Estimate CATE self.cate = self.predict_cate(X) - - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): + def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): # Split data to two independent samples of equal size ( X0, X1, y0, y1, treated0, treated1 - ) = train_test_split(X, y, treated, stratify=treated, test_size=.5) - - treated_model, untreated_model, propensity_score_model, pseudo_outcome_model = self.models.values() - - # Second iteration is the cross-fitting step. + ) = train_test_split(X, y, treated, stratify=treated, test_size=.5) + + treated_model = self.models["treated"] + untreated_model = self.models["untreated"] + propensity_score_model = self.models["propensity"] + pseudo_outcome_model = self.models["pseudo_outcome"] + + # Second iteration is the cross-fitting step. second_iteration = False for i in range(2): # Split data to treated and untreated subsets @@ -457,33 +622,36 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): g = propensity_score_model.predict_proba(X1)[:, 1] mu_0 = untreated_model.predict(X1) mu_1 = treated_model.predict(X1) - mu_w = np.where(treated1==0, mu_0, mu_1) + mu_w = np.where(treated1 == 0, mu_0, mu_1) pseudo_outcome = ( (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 - ) + ) # Fit pseudo-outcome model _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) - + if self.cross_fitting and not second_iteration: # Swap data and estimators (X0, X1) = (X1, X0) (y0, y1) = (y1, y0) (treated0, treated1) = (treated1, treated0) - - treated_model, untreated_model, propensity_score_model, pseudo_outcome_model = self.cross_fitted_models.values() + + treated_model = self.cross_fitted_models["treated"] + untreated_model = self.cross_fitted_models["untreated"] + propensity_score_model = self.cross_fitted_models["propensity"] + pseudo_outcome_model = self.cross_fitted_models["pseudo_outcome"] second_iteration = True else: return self return self - def predict_cate(self, X): + def predict_cate(self, X: pd.DataFrame) -> np.array: pred1 = self.models["pseudo_outcome"].predict(X) - + if self.cross_fitting: pred2 = self.cross_fitted_models["pseudo_outcome"].predict(X) return (pred1 + pred2) / 2 - - return pred1 \ No newline at end of file + + return pred1 From 21d0b1552c50fc9621ca5ff80287c8e9f7bf41a6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 12 Mar 2023 20:25:27 +0100 Subject: [PATCH 31/57] fixed a dependency --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 1b5292c9..9e1d002a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ dependencies = [ "scipy", "seaborn>=0.11.2", "xarray>=v2022.11.0", + "pymc_bart" ] # List additional groups of dependencies here (e.g. development dependencies). Users From c4f124b6eb03a330d21f7ddb58f2566b45c0a85b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 12 Mar 2023 20:43:16 +0100 Subject: [PATCH 32/57] improvements on LogisticRegression --- causalpy/pymc_models.py | 63 +++++++++++++++++++++++++++++++++++------ 1 file changed, 54 insertions(+), 9 deletions(-) diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index aa1c7919..84b7f6c3 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -4,9 +4,9 @@ import numpy as np import pandas as pd import pymc as pm -from arviz import r2_score import pymc_bart as pmb - +from arviz import r2_score +from pymc.distributions.distribution import DistributionMeta class ModelBuilder(pm.Model): @@ -128,17 +128,62 @@ def __init__(self, sample_kwargs=None, m=20, sigma=1): def build_model(self, X, y, coords=None): with self: self.add_coords(coords) - X = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) - mu = pmb.BART("mu", X, y, m=self.m, dims="obs_ind") + X_ = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) + mu = pmb.BART("mu", X_, y, m=self.m, dims="obs_ind") pm.Normal("y_hat", mu=mu, sigma=self.sigma, observed=y, dims="obs_ind") + class LogisticRegression(ModelBuilder): - "Custom PyMC model for logistic regression." + """ + Custom PyMC model for logistic regression. + + Parameters + ---------- + coeff_distribution : PyMC distribution. + Prior distribution of coefficient vector. + distribution_kwargs + Keyword arguments for prior distribution. + sample_kwargs + Keyword arguments for sampler. + + Examples + -------- + >>> import numpy as np + >>> import pymc as pm + >>> from causalpy.pymc_models import LogisticRegression + >>> + >>> X = np.random.rand(10, 10) + >>> y = np.random.rand(10) + >>> m = LogisticRegression( + >>> coeff_distribution=pm.Cauchy, + >>> coeff_distribution_kwargs={"alpha": 0, "beta": 1} + >>> ) + >>> + >>> m.fit(X, y) + """ + + def __init__( + self, + sample_kwargs=None, + coeff_distribution: DistributionMeta = pm.Normal, + coeff_distribution_kwargs: Optional[dict[str, Any]] = None, + ): + self.coeff_distribution = coeff_distribution + if coeff_distribution_kwargs is None: + self.coeff_distribution_kwargs = {"mu": 0, "sigma": 50} + else: + self.coeff_distribution_kwargs = coeff_distribution_kwargs + + super().__init__(sample_kwargs) def build_model(self, X, y, coords) -> None: with self: self.add_coords(coords) - X = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) - beta = pm.Normal("beta", 0, 50, dims="coeffs") - mu = pm.math.sigmoid(pm.math.dot(X, beta)) - pm.Bernoulli("yhat", mu, observed=y) + X_ = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) + beta = self.coeff_distribution( + "beta", dims="coeffs", **self.coeff_distribution_kwargs + ) + mu = pm.Deterministic( + "mu", pm.math.sigmoid(pm.math.dot(X_, beta)), dims="obs_ind" + ) + pm.Bernoulli("y_hat", mu, observed=y) From 90fddd78ac082330a613eaedc796ee5db34e081a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 12 Mar 2023 20:47:51 +0100 Subject: [PATCH 33/57] several improvements --- causalpy/pymc_meta_learners.py | 335 +++++++++++++++++++++++++-------- causalpy/skl_meta_learners.py | 192 +++++++++++-------- 2 files changed, 368 insertions(+), 159 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index da96f896..33f281f9 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -1,24 +1,64 @@ -import pandas as pd -import numpy as np -import pymc as pm -import xarray as xa +from typing import Any, Dict +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split + +from causalpy.pymc_models import LogisticRegression, ModelBuilder +from causalpy.skl_meta_learners import ( + DRLearner, + MetaLearner, + SLearner, + TLearner, + XLearner, +) from causalpy.utils import _fit -from causalpy.skl_meta_learners import MetaLearner, SLearner, TLearner, XLearner, DRLearner -from causalpy.pymc_models import LogisticRegression + class BayesianMetaLearner(MetaLearner): "Base class for PyMC based meta-learners." - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): - "Fits model." + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): + """ + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + coords : Dict[str, Any]. + Dictionary containing the keys coeffs and obs_indx. + """ raise NotImplementedError() - - class BayesianSLearner(SLearner, BayesianMetaLearner): - "PyMC version of S-Learner." + """ + Implements PyMC version of S-learner described in [1]. S-learner estimates + conditional average treatment effect with the use of a single model. + + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners + for estimating heterogeneous treatment effects using machine learning. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : causalpy.pymc_models.ModelBuilder. + Base learner. + """ def predict_cate(self, X: pd.DataFrame) -> np.array: X_untreated = X.assign(treatment=0) @@ -28,13 +68,33 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: pred_treated = m.predict(X_treated)["posterior_predictive"].mu pred_untreated = m.predict(X_untreated)["posterior_predictive"].mu - cate = pred_treated - pred_untreated - - return cate + return pred_treated - pred_untreated class BayesianTLearner(TLearner, BayesianMetaLearner): - "PyMC version of T-Learner." + """ + Implements of T-learner described in [1]. T-learner fits two separate models to + estimate conditional average treatment effect. + +[1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners + for estimating heterogeneous treatment effects using machine learning. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : causalpy.pymc_models.ModelBuilder. + If specified, it will be used both as treated and untreated model. Either + model or both of treated_model and untreated_model have to be specified. + treated_model: causalpy.pymc_models.ModelBuilder. + Model used for predicting target vector for treated values. + untreated_model: causalpy.pymc_models.ModelBuilder. + Model used for predicting target vector for untreated values. + """ def predict_cate(self, X: pd.DataFrame) -> np.array: treated_model = self.models["treated"] @@ -43,25 +103,56 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: pred_treated = treated_model.predict(X)["posterior_predictive"].mu pred_untreated = untreated_model.predict(X)["posterior_predictive"].mu - cate = pred_treated - pred_untreated - - return cate + return pred_treated - pred_untreated class BayesianXLearner(XLearner, BayesianMetaLearner): - "PyMC version of X-Learner." + """ + Implements of PyMC version of X-learner introduced in [1]. X-learner estimates + conditional average treatment effect with the use of five separate models. + + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners + for estimating heterogeneous treatment effects using machine learning. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : causalpy.pymc_models.ModelBuilder. + If specified, it will be used in all of the subregressions, except for the + propensity_score_model. Either model or all of treated_model, + untreated_model, treated_cate_estimator and untreated_cate_estimator have to + be specified. + treated_model : causalpy.pymc_models.ModelBuilder. + Model used for predicting target vector for treated values. + untreated_model : causalpy.pymc_models.ModelBuilder. + Model used for predicting target vector for untreated values. + untreated_cate_estimator : causalpy.pymc_models.ModelBuilder. + Model used for CATE estimation on untreated data. + treated_cate_estimator : causalpy.pymc_models.ModelBuilder. + Model used for CATE estimation on treated data. + propensity_score_model : causalpy.pymc_models.ModelBuilder, + default = causalpy.pymc_models.LogisticRegression(). + Model used for propensity score estimation. Output values should be in the + interval [0, 1]. + """ def __init__( self, - X, - y, - treated, - model=None, - treated_model=None, - untreated_model=None, - treated_cate_estimator=None, - untreated_cate_estimator=None, - propensity_score_model=LogisticRegression() + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model: ModelBuilder = None, + treated_model: ModelBuilder = None, + untreated_model: ModelBuilder = None, + treated_cate_estimator: ModelBuilder = None, + untreated_cate_estimator: ModelBuilder = None, + propensity_score_model: ModelBuilder = LogisticRegression(), ) -> None: super().__init__( @@ -73,17 +164,21 @@ def __init__( untreated_model, treated_cate_estimator, untreated_cate_estimator, - propensity_score_model - ) - - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): - ( - treated_model, - untreated_model, - treated_cate_estimator, - untreated_cate_estimator, - propensity_score_model - ) = self.models.values() + propensity_score_model, + ) + + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): + treated_model = self.models["treated"] + untreated_model = self.models["untreated"] + treated_cate_estimator = self.models["treated_cate"] + untreated_cate_estimator = self.models["untreated_cate"] + propensity_score_model = self.models["propensity"] # Split data to treated and untreated subsets X_t, y_t = X[treated == 1], y[treated == 1] @@ -94,19 +189,15 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): _fit(untreated_model, X_u, y_u, coords) pred_u_t = ( - untreated_model - .predict(X_t)["posterior_predictive"] - .mu - .mean(dim=["chain", "draw"]) + untreated_model.predict(X_t)["posterior_predictive"] + .mu.mean(dim=["chain", "draw"]) .to_numpy() - ) + ) pred_t_u = ( - treated_model - .predict(X_u)["posterior_predictive"] - .mu - .mean(dim=["chain", "draw"]) + treated_model.predict(X_u)["posterior_predictive"] + .mu.mean(dim=["chain", "draw"]) .to_numpy() - ) + ) tau_t = y_t - pred_u_t tau_u = y_u - pred_t_u @@ -119,16 +210,9 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords=None): _fit(propensity_score_model, X, treated, coords) return self - - def _compute_cate(self, X): - cate_t = self.models["treated_cate"].predict(X)["posterior_predictive"].mu - cate_u = self.models["treated_cate"].predict(X)["posterior_predictive"].mu - g = self.models["propensity"].predict(X)["posterior_predictive"].mu - return g * cate_u + (1 - g) * cate_t - def predict_cate(self, X: pd.DataFrame) -> np.array: treated_model = self.models["treated_cate"] - untreated_model = self.models["untreated_cate"] + untreated_model = self.models["untreated_cate"] cate_estimate_treated = treated_model.predict(X)["posterior_predictive"].mu cate_estimate_untreated = untreated_model.predict(X)["posterior_predictive"].mu @@ -138,38 +222,125 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: class BayesianDRLearner(DRLearner, BayesianMetaLearner): - "PyMC version of DR-Learner." + """ + Implements of DR-learner also known as doubly robust learner as described in [1]. + + [1] Curth, Alicia, Mihaela van der Schaar. + Nonparametric estimation of heterogeneous treatment effects: From theory to + learning algorithms. International Conference on Artificial Intelligence and + Statistics, pp. 1810-1818 (2021). + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Training data. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + model : causalpy.pymc_models.ModelBuilder. + If specified, it will be used in all of the subregressions, except for + the propensity_score_model. Either model or all of treated_model, + untreated_model, treated_cate_estimator and untreated_cate_estimator + have to be specified. + treated_model : causalpy.pymc_models.ModelBuilder. + Model used for predicting target vector for treated values. + untreated_model : causalpy.pymc_models.ModelBuilder. + Model used for predicting target vector for untreated values. + pseudo_outcome_model : causalpy.pymc_models.ModelBuilder. + Model used for pseudo-outcome estimation. + propensity_score_model : causalpy.pymc_models.ModelBuilder, + default = causalpy.pymc_models.LogisticRegression(). + Model used for propensity score estimation. + cross_fitting : bool, default=False. + If True, performs a cross fitting step. + """ def __init__( self, - X, - y, - treated, - model=None, - treated_model=None, - untreated_model=None, - propensity_score_model=LogisticRegression() + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model: ModelBuilder = None, + treated_model: ModelBuilder = None, + untreated_model: ModelBuilder = None, + propensity_score_model: ModelBuilder = LogisticRegression(), + ) -> None: + super().__init__( + X, y, treated, model, treated_model, untreated_model, propensity_score_model + ) + self.cate = self.cate.mean(dim=["chain", "draw"]) + + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, ): - super().__init__(X, y, treated, model, treated_model, untreated_model, propensity_score_model) + # Split data to two independent samples of equal size + ( + X0, X1, + y0, y1, + treated0, treated1 + ) = train_test_split(X, y, treated, stratify=treated, test_size=.5) - def predict_cate(self, X: pd.DataFrame) -> np.array: - m1 = self.models["treated"].predict(X) - m0 = self.models["untreated"].predict(X) - return m1 - m0 - - def _compute_cate(self, X, y, treated): - g = self.models["propensity"].predict(X)["posterior_predictive"].mu - m0 = self.models["untreated"].predict(X)["posterior_predictive"].mu - m1 = self.models["treated"].predict(X)["posterior_predictive"].mu + treated_model = self.models["treated"] + untreated_model = self.models["untreated"] + propensity_score_model = self.models["propensity"] + pseudo_outcome_model = self.models["pseudo_outcome"] + + # Second iteration is the cross-fitting step. + second_iteration = False + for _ in range(2): + # Split data to treated and untreated subsets + X_t, y_t = X0[treated0 == 1], y0[treated0 == 1] + X_u, y_u = X0[treated0 == 0], y0[treated0 == 0] + + # Estimate response functions + _fit(treated_model, X_t, y_t, coords) + _fit(untreated_model, X_u, y_u, coords) + + # Fit propensity score model + _fit(propensity_score_model, X, treated, coords) + + g = propensity_score_model.predict(X1)["posterior_predictive"].mu + mu_0 = untreated_model.predict(X1)["posterior_predictive"].mu + mu_1 = treated_model.predict(X1)["posterior_predictive"].mu + mu_w = np.where(treated1 == 0, mu_0, mu_1) + + pseudo_outcome = ( + (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 + ) + + # Fit pseudo-outcome model + _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) + if self.cross_fitting and not second_iteration: + # Swap data and estimators + (X0, X1) = (X1, X0) + (y0, y1) = (y1, y0) + (treated0, treated1) = (treated1, treated0) - # Broadcast target and treated variables to the size of the predictions - y0 = xa.DataArray(y, dims="obs_ind") - y0 = xa.broadcast(y0, m0)[0] + treated_model = self.cross_fitted_models["treated"] + untreated_model = self.cross_fitted_models["untreated"] + propensity_score_model = self.cross_fitted_models["propensity"] + pseudo_outcome_model = self.cross_fitted_models["pseudo_outcome"] + second_iteration = True + else: + return self + + return self - t0 = xa.DataArray(treated, dims="obs_ind") - t0 = xa.broadcast(t0, m0)[0] + def predict_cate(self, X: pd.DataFrame) -> pd.Series: + pred = self.models["pseudo_outcome"].predict(X)["posterior_predictive"].mu - cate = (t0 * (y0 - m1) / g + m1 - ((1 - t0) * (y0 - m0) / (1 - g) + m0)) + if self.cross_fitting: + pred2 = ( + self.cross_fitted_models["pseudo_outcome"] + .predict(X) + ["posterior_predictive"] + .mu) + pred = (pred + pred2) / 2 - return cate \ No newline at end of file + return pd.Series(pred, index=self.index) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 00d7c6ec..91966b12 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -1,4 +1,6 @@ from copy import deepcopy +from typing import Any, Dict + import matplotlib.pyplot as plt import numpy as np import pandas as pd @@ -6,9 +8,8 @@ from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.utils import check_consistent_length -from typing import Any, Dict -from causalpy.utils import _is_variable_dummy_coded, _fit +from causalpy.utils import _fit, _is_variable_dummy_coded class MetaLearner: @@ -32,7 +33,7 @@ def __init__(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series) -> None: self.labels = X.columns self.index = X.index - def predict_cate(self, X: pd.DataFrame) -> np.array: + def predict_cate(self, X: pd.DataFrame) -> pd.Series: """ Predict out-of-sample conditional average treatment effect on given input X. For in-sample treatement effect self.cate should be used. @@ -45,9 +46,9 @@ def predict_cate(self, X: pd.DataFrame) -> np.array: def predict_ate(self, X: pd.DataFrame) -> np.float64: """ - Predict out-of-sample average treatment effect on given input X. For in-sample treatement - effect self.ate() should be used. - + Predict out-of-sample average treatment effect on given input X. For in-sample + treatement effect self.ate() should be used. + Parameters ---------- X : pandas.DataFrame of shape (n_samples, n_featues). @@ -58,9 +59,15 @@ def ate(self) -> np.float64: "Returns in-sample average treatement effect." return self.cate.mean() - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): """Fits model. - + Parameters ---------- X : pandas.DataFrame of shape (n_samples, n_featues). @@ -76,9 +83,9 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[st def summary(self) -> None: "Prints summary." - print(f"Number of observations: {self.X.shape[0]}") - print(f"Number of treated observations: {self.treated.sum()}") - print(f"Average treatement effect (ATE): {self.predict_ate(self.X.shape[0])}") + print(f"Number of observations: {self.X.shape[0]}") + print(f"Number of treated observations: {self.treated.sum()}") + print(f"Average treatement effect (ATE): {self.predict_ate(self.X.shape[0])}") def plot(self): "Plots results. Content is undecided yet." @@ -88,6 +95,15 @@ def plot(self): class SkMetaLearner(MetaLearner): "Base class for sklearn based meta-learners." + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): + raise NotImplementedError() + def bootstrap( self, X_ins: pd.DataFrame, @@ -154,11 +170,11 @@ def ate_confidence_interval( y: pd.Series, treated: pd.Series, X: pd.DataFrame = None, - q: float = .05, + q: float = 0.05, n_iter: int = 1000, ) -> tuple: """Estimates confidence intervals for ATE on X using bootstraping. - + Parameters ---------- X_ins : pandas.DataFrame of shape (n_samples, n_featues). @@ -170,7 +186,7 @@ def ate_confidence_interval( X : pandas.DataFrame of shape (_, n_features). Data to predict on. q : float, default=.05. - Quantile to compute. Should be between in the interval [0, 1]. + Quantile to compute. Should be between in the interval [0, 1]. n_iter : int, default = 1000. Number of bootstrap iterations to perform. """ @@ -184,11 +200,11 @@ def cate_confidence_interval( y: pd.Series, treated: pd.Series, X: pd.DataFrame = None, - q: float = .05, + q: float = 0.05, n_iter: int = 1000, ) -> np.array: """Estimates confidence intervals for CATE on X using bootstraping. - + Parameters ---------- X_ins : pandas.DataFrame of shape (n_samples, n_featues). @@ -200,7 +216,7 @@ def cate_confidence_interval( X : pandas.DataFrame of shape (_, n_features). Data to predict on. q : float, default=.05. - Quantile to compute. Should be between in the interval [0, 1]. + Quantile to compute. Should be between in the interval [0, 1]. n_iter : int, default = 1000. Number of bootstrap iterations to perform.""" cates = self.bootstrap(X_ins, y, treated, X, n_iter) @@ -217,11 +233,10 @@ def bias( y: pd.Series, treated: pd.Series, X: pd.DataFrame = None, - q: float = .05, n_iter: int = 1000, ) -> np.float64: """Calculates bootstrap estimate of bias of CATE estimator. - + Parameters ---------- X_ins : pandas.DataFrame of shape (n_samples, n_featues). @@ -233,7 +248,7 @@ def bias( X : pandas.DataFrame of shape (_, n_features). Data to predict on. q : float, default=.05. - Quantile to compute. Should be between in the interval [0, 1]. + Quantile to compute. Should be between in the interval [0, 1]. n_iter : int, default = 1000. Number of bootstrap iterations to perform.""" if X is None: @@ -270,8 +285,8 @@ class SLearner(SkMetaLearner): Implements of S-learner described in [1]. S-learner estimates conditional average treatment effect with the use of a single model. - [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. - Metalearners for estimating heterogeneous treatment effects using machine learning. + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners + for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. Parameters @@ -292,35 +307,38 @@ def __init__( super().__init__(X=X, y=y, treated=treated) self.models["model"] = model - COORDS = { - "coeffs": list(self.labels) + ["treated"], - "obs_indx": self.index - } + COORDS = {"coeffs": list(self.labels) + ["treated"], "obs_indx": self.index} self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): X_t = X.assign(treatment=treated) _fit(model=self.models["model"], X=X_t, y=y, coords=coords) return self - def predict_cate(self, X: pd.DataFrame) -> np.array: + def predict_cate(self, X: pd.DataFrame) -> pd.Series: X_control = X.assign(treatment=0) X_treated = X.assign(treatment=1) m = self.models["model"] - return m.predict(X_treated) - m.predict(X_control) + return pd.Series(m.predict(X_treated) - m.predict(X_control), index=self.index) class TLearner(SkMetaLearner): """ - Implements of T-learner described in [1]. T-learner fits two separate models to estimate - conditional average treatment effect. + Implements of T-learner described in [1]. T-learner fits two separate models to + estimate conditional average treatment effect. - [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. - Metalearners for estimating heterogeneous treatment effects using machine learning. + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners + for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. - + Parameters ---------- X : pandas.DataFrame of shape (n_samples, n_featues). @@ -330,12 +348,12 @@ class TLearner(SkMetaLearner): treated : pandas.Series of shape (n_samples, ). Treatement assignment indicator consisting of zeros and ones. model : sklearn.base.RegressorMixin. - If specified, it will be used both as treated and untreated model. Either model or - both of treated_model and untreated_model have to be specified. + If specified, it will be used both as treated and untreated model. Either + model or both of treated_model and untreated_model have to be specified. treated_model: sklearn.base.RegressorMixin. - Model used for predicting target vector for treated values. + Model used for predicting target vector for treated values. untreated_model: sklearn.base.RegressorMixin. - Model used for predicting target vector for untreated values. + Model used for predicting target vector for untreated values. """ def __init__( @@ -371,26 +389,34 @@ def __init__( self.fit(X, y, treated, coords=COORDS) self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): X_t, y_t = X[treated == 1], y[treated == 1] X_u, y_u = X[treated == 0], y[treated == 0] _fit(model=self.models["treated"], X=X_t, y=y_t, coords=coords) _fit(model=self.models["untreated"], X=X_u, y=y_u, coords=coords) return self - def predict_cate(self, X: pd.DataFrame) -> np.array: + def predict_cate(self, X: pd.DataFrame) -> pd.Series: treated_model = self.models["treated"] untreated_model = self.models["untreated"] - return treated_model.predict(X) - untreated_model.predict(X) + return pd.Series( + treated_model.predict(X) - untreated_model.predict(X), index=self.index + ) class XLearner(SkMetaLearner): """ - Implements of X-learner introduced in [1]. X-learner estimates conditional average treatment - effect with the use of five separate models. + Implements of X-learner introduced in [1]. X-learner estimates conditional average + treatment effect with the use of five separate models. - [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. - Metalearners for estimating heterogeneous treatment effects using machine learning. + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners + for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. Parameters @@ -403,10 +429,11 @@ class XLearner(SkMetaLearner): Treatement assignment indicator consisting of zeros and ones. model : sklearn.base.RegressorMixin. If specified, it will be used in all of the subregressions, except for the - propensity_score_model. Either model or all of treated_model, untreated_model, - treated_cate_estimator and untreated_cate_estimator have to be specified. + propensity_score_model. Either model or all of treated_model, + untreated_model, treated_cate_estimator and untreated_cate_estimator have to + be specified. treated_model : sklearn.base.RegressorMixin. - Model used for predicting target vector for treated values. + Model used for predicting target vector for treated values. untreated_model : sklearn.base.RegressorMixin. Model used for predicting target vector for untreated values. untreated_cate_estimator : sklearn.base.RegressorMixin @@ -454,7 +481,7 @@ def __init__( "untreated": untreated_model, "treated_cate": treated_cate_estimator, "untreated_cate": untreated_cate_estimator, - "propensity": propensity_score_model + "propensity": propensity_score_model, } COORDS = {"coeffs": self.labels, "obs_indx": self.index} @@ -471,7 +498,13 @@ def _compute_cate(self, X: pd.Series): g = self.models["propensity"].predict_proba(X)[:, 1] return g * cate_u + (1 - g) * cate_t - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): treated_model = self.models["treated"] untreated_model = self.models["untreated"] treated_cate_estimator = self.models["treated_cate"] @@ -497,13 +530,13 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[st _fit(propensity_score_model, X, treated, coords) return self - def predict_cate(self, X: pd.DataFrame) -> np.array: + def predict_cate(self, X: pd.DataFrame) -> pd.Series: cate_estimate_treated = self.models["treated_cate"].predict(X) cate_estimate_untreated = self.models["untreated_cate"].predict(X) - g = self.models["propensity"].predict_proba(X)[:, 1] - return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated + cate = g * cate_estimate_untreated + (1 - g) * cate_estimate_treated + return pd.Series(cate, index=self.index) class DRLearner(SkMetaLearner): @@ -511,9 +544,10 @@ class DRLearner(SkMetaLearner): Implements of DR-learner also known as doubly robust learner as described in [1]. [1] Curth, Alicia, Mihaela van der Schaar. - Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms. - International Conference on Artificial Intelligence and Statistics, pp. 1810-1818 (2021). - + Nonparametric estimation of heterogeneous treatment effects: From theory to + learning algorithms. International Conference on Artificial Intelligence and + Statistics, pp. 1810-1818 (2021). + Parameters ---------- X : pandas.DataFrame of shape (n_samples, n_featues). @@ -523,11 +557,12 @@ class DRLearner(SkMetaLearner): treated : pandas.Series of shape (n_samples, ). Treatement assignment indicator consisting of zeros and ones. model : sklearn.base.RegressorMixin. - If specified, it will be used in all of the subregressions, except for the - propensity_score_model. Either model or all of treated_model, untreated_model, - treated_cate_estimator and untreated_cate_estimator have to be specified. + If specified, it will be used in all of the subregressions, except for + the propensity_score_model. Either model or all of treated_model, + untreated_model, treated_cate_estimator and untreated_cate_estimator + have to be specified. treated_model : sklearn.base.RegressorMixin. - Model used for predicting target vector for treated values. + Model used for predicting target vector for treated values. untreated_model : sklearn.base.RegressorMixin. Model used for predicting target vector for untreated values. pseudo_outcome_model : sklearn.base.RegressorMixin @@ -576,13 +611,14 @@ def __init__( "treated": treated_model, "untreated": untreated_model, "propensity": propensity_score_model, - "pseudo_outcome": pseudo_outcome_model + "pseudo_outcome": pseudo_outcome_model, } cross_fitted_models = {} if self.cross_fitting: - cross_fitted_models = {key: deepcopy(value) - for key, value in self.models.items()} + cross_fitted_models = { + key: deepcopy(value) for key, value in self.models.items() + } self.cross_fitted_models = cross_fitted_models @@ -592,13 +628,17 @@ def __init__( # Estimate CATE self.cate = self.predict_cate(X) - def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[str, Any] = None): + def fit( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, + ): # Split data to two independent samples of equal size - ( - X0, X1, - y0, y1, - treated0, treated1 - ) = train_test_split(X, y, treated, stratify=treated, test_size=.5) + (X0, X1, y0, y1, treated0, treated1) = train_test_split( + X, y, treated, stratify=treated, test_size=0.5 + ) treated_model = self.models["treated"] untreated_model = self.models["untreated"] @@ -607,7 +647,7 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[st # Second iteration is the cross-fitting step. second_iteration = False - for i in range(2): + for _ in range(2): # Split data to treated and untreated subsets X_t, y_t = X0[treated0 == 1], y0[treated0 == 1] X_u, y_u = X0[treated0 == 0], y0[treated0 == 0] @@ -624,9 +664,7 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[st mu_1 = treated_model.predict(X1) mu_w = np.where(treated1 == 0, mu_0, mu_1) - pseudo_outcome = ( - (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 - ) + pseudo_outcome = (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 # Fit pseudo-outcome model _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) @@ -647,11 +685,11 @@ def fit(self, X: pd.DataFrame, y: pd.Series, treated: pd.Series, coords: Dict[st return self - def predict_cate(self, X: pd.DataFrame) -> np.array: - pred1 = self.models["pseudo_outcome"].predict(X) + def predict_cate(self, X: pd.DataFrame) -> pd.Series: + pred = self.models["pseudo_outcome"].predict(X) if self.cross_fitting: pred2 = self.cross_fitted_models["pseudo_outcome"].predict(X) - return (pred1 + pred2) / 2 + pred = (pred + pred2) / 2 - return pred1 + return pd.Series(pred, index=self.index) From 917216ca5a022941c325bb7f29dc0fba0a5c477b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 15 Mar 2023 14:48:34 +0100 Subject: [PATCH 34/57] BayesianDR now works --- causalpy/pymc_meta_learners.py | 104 ++++++++++++++++++++------------- causalpy/pymc_models.py | 2 +- 2 files changed, 63 insertions(+), 43 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index 33f281f9..9fcbd3b8 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -1,5 +1,6 @@ -from typing import Any, Dict +from typing import Any, Dict, Optional +import arviz as az import numpy as np import pandas as pd from sklearn.model_selection import train_test_split @@ -78,7 +79,7 @@ class BayesianTLearner(TLearner, BayesianMetaLearner): [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. - Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. + Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. Parameters ---------- X : pandas.DataFrame of shape (n_samples, n_featues). @@ -91,7 +92,7 @@ class BayesianTLearner(TLearner, BayesianMetaLearner): If specified, it will be used both as treated and untreated model. Either model or both of treated_model and untreated_model have to be specified. treated_model: causalpy.pymc_models.ModelBuilder. - Model used for predicting target vector for treated values. + Model used for predicting target vector for treated values. untreated_model: causalpy.pymc_models.ModelBuilder. Model used for predicting target vector for untreated values. """ @@ -126,8 +127,8 @@ class BayesianXLearner(XLearner, BayesianMetaLearner): model : causalpy.pymc_models.ModelBuilder. If specified, it will be used in all of the subregressions, except for the propensity_score_model. Either model or all of treated_model, - untreated_model, treated_cate_estimator and untreated_cate_estimator have to - be specified. + untreated_model, treated_cate_estimator and untreated_cate_estimator have + to be specified. treated_model : causalpy.pymc_models.ModelBuilder. Model used for predicting target vector for treated values. untreated_model : causalpy.pymc_models.ModelBuilder. @@ -143,29 +144,27 @@ class BayesianXLearner(XLearner, BayesianMetaLearner): """ def __init__( - self, - X: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - model: ModelBuilder = None, - treated_model: ModelBuilder = None, - untreated_model: ModelBuilder = None, - treated_cate_estimator: ModelBuilder = None, - untreated_cate_estimator: ModelBuilder = None, - propensity_score_model: ModelBuilder = LogisticRegression(), - ) -> None: - + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model: Optional[ModelBuilder] = None, + treated_model: Optional[ModelBuilder] = None, + untreated_model: Optional[ModelBuilder] = None, + treated_cate_estimator: Optional[ModelBuilder] = None, + untreated_cate_estimator: Optional[ModelBuilder] = None, + propensity_score_model: Optional[ModelBuilder] = None, + ): super().__init__( X, y, - treated, - model, + treated, model, treated_model, untreated_model, treated_cate_estimator, untreated_cate_estimator, propensity_score_model, - ) + ) def fit( self, @@ -188,15 +187,15 @@ def fit( _fit(treated_model, X_t, y_t, coords) _fit(untreated_model, X_u, y_u, coords) - pred_u_t = ( - untreated_model.predict(X_t)["posterior_predictive"] - .mu.mean(dim=["chain", "draw"]) - .to_numpy() + pred_u_t = az.extract( + untreated_model.predict(X_t), + group="posterior_predictive", + var_names="mu" ) - pred_t_u = ( - treated_model.predict(X_u)["posterior_predictive"] - .mu.mean(dim=["chain", "draw"]) - .to_numpy() + pred_t_u = az.extract( + treated_model.predict(X_u), + group="posterior_predictive", + var_names="mu" ) tau_t = y_t - pred_u_t @@ -250,7 +249,7 @@ class BayesianDRLearner(DRLearner, BayesianMetaLearner): pseudo_outcome_model : causalpy.pymc_models.ModelBuilder. Model used for pseudo-outcome estimation. propensity_score_model : causalpy.pymc_models.ModelBuilder, - default = causalpy.pymc_models.LogisticRegression(). + default = causalpy.pymc_models.LogisticRegression(). Model used for propensity score estimation. cross_fitting : bool, default=False. If True, performs a cross fitting step. @@ -261,15 +260,24 @@ def __init__( X: pd.DataFrame, y: pd.Series, treated: pd.Series, - model: ModelBuilder = None, - treated_model: ModelBuilder = None, - untreated_model: ModelBuilder = None, - propensity_score_model: ModelBuilder = LogisticRegression(), - ) -> None: + model: Optional[ModelBuilder] = None, + treated_model: Optional[ModelBuilder] = None, + untreated_model: Optional[ModelBuilder] = None, + pseudo_outcome_model: Optional[ModelBuilder] = None, + propensity_score_model: Optional[ModelBuilder] = LogisticRegression(), + cross_fitting: bool = False, + ): super().__init__( - X, y, treated, model, treated_model, untreated_model, propensity_score_model + X, + y, + treated, + model, + treated_model, + untreated_model, + pseudo_outcome_model, + propensity_score_model, + cross_fitting, ) - self.cate = self.cate.mean(dim=["chain", "draw"]) def fit( self, @@ -302,17 +310,28 @@ def fit( _fit(untreated_model, X_u, y_u, coords) # Fit propensity score model - _fit(propensity_score_model, X, treated, coords) - - g = propensity_score_model.predict(X1)["posterior_predictive"].mu - mu_0 = untreated_model.predict(X1)["posterior_predictive"].mu - mu_1 = treated_model.predict(X1)["posterior_predictive"].mu + _fit(propensity_score_model, X0, treated0, coords) + + g = az.extract( + propensity_score_model.predict(X1), + group="posterior_predictive", + var_names="mu").mean(axis=1) + mu_0 = az.extract( + untreated_model.predict(X1), + group="posterior_predictive", + var_names="mu").mean(axis=1) + mu_1 = az.extract( + treated_model.predict(X1), + group="posterior_predictive", + var_names="mu").mean(axis=1) + mu_w = np.where(treated1 == 0, mu_0, mu_1) pseudo_outcome = ( (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 ) + # Fit pseudo-outcome model _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) @@ -326,6 +345,7 @@ def fit( untreated_model = self.cross_fitted_models["untreated"] propensity_score_model = self.cross_fitted_models["propensity"] pseudo_outcome_model = self.cross_fitted_models["pseudo_outcome"] + second_iteration = True else: return self @@ -343,4 +363,4 @@ def predict_cate(self, X: pd.DataFrame) -> pd.Series: .mu) pred = (pred + pred2) / 2 - return pd.Series(pred, index=self.index) + return pred \ No newline at end of file diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 84b7f6c3..140d6439 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -186,4 +186,4 @@ def build_model(self, X, y, coords) -> None: mu = pm.Deterministic( "mu", pm.math.sigmoid(pm.math.dot(X_, beta)), dims="obs_ind" ) - pm.Bernoulli("y_hat", mu, observed=y) + pm.Bernoulli("y_hat", mu, observed=y, dims="obs_ind") From bb588b94076190563c116e4adb7785c36e2aa48f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 15 Mar 2023 21:19:23 +0100 Subject: [PATCH 35/57] BayesianXLearner now works --- causalpy/pymc_meta_learners.py | 29 ++++++++++++++++++++++------- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index 9fcbd3b8..92b7dfaa 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -4,6 +4,8 @@ import numpy as np import pandas as pd from sklearn.model_selection import train_test_split +from xarray.core.dataarray import DataArray + from causalpy.pymc_models import LogisticRegression, ModelBuilder from causalpy.skl_meta_learners import ( @@ -27,6 +29,8 @@ def fit( coords: Dict[str, Any] = None, ): """ + Fits base-learners. + Parameters ---------- X : pandas.DataFrame of shape (n_samples, n_featues). @@ -38,6 +42,17 @@ def fit( Dictionary containing the keys coeffs and obs_indx. """ raise NotImplementedError() + + def predict_cate(self, X: pd.DataFrame) -> DataArray: + """ + Predicts distribution of treatement effect. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Feature matrix. + """ + raise NotImplementedError() class BayesianSLearner(SLearner, BayesianMetaLearner): @@ -61,7 +76,7 @@ class BayesianSLearner(SLearner, BayesianMetaLearner): Base learner. """ - def predict_cate(self, X: pd.DataFrame) -> np.array: + def predict_cate(self, X: pd.DataFrame) -> DataArray: X_untreated = X.assign(treatment=0) X_treated = X.assign(treatment=1) m = self.models["model"] @@ -77,7 +92,7 @@ class BayesianTLearner(TLearner, BayesianMetaLearner): Implements of T-learner described in [1]. T-learner fits two separate models to estimate conditional average treatment effect. -[1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners + [1] Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165. Parameters @@ -97,7 +112,7 @@ class BayesianTLearner(TLearner, BayesianMetaLearner): Model used for predicting target vector for untreated values. """ - def predict_cate(self, X: pd.DataFrame) -> np.array: + def predict_cate(self, X: pd.DataFrame) -> DataArray: treated_model = self.models["treated"] untreated_model = self.models["untreated"] @@ -191,12 +206,12 @@ def fit( untreated_model.predict(X_t), group="posterior_predictive", var_names="mu" - ) + ).mean(axis=1) pred_t_u = az.extract( treated_model.predict(X_u), group="posterior_predictive", var_names="mu" - ) + ).mean(axis=1) tau_t = y_t - pred_u_t tau_u = y_u - pred_t_u @@ -209,7 +224,7 @@ def fit( _fit(propensity_score_model, X, treated, coords) return self - def predict_cate(self, X: pd.DataFrame) -> np.array: + def predict_cate(self, X: pd.DataFrame) -> DataArray: treated_model = self.models["treated_cate"] untreated_model = self.models["untreated_cate"] @@ -352,7 +367,7 @@ def fit( return self - def predict_cate(self, X: pd.DataFrame) -> pd.Series: + def predict_cate(self, X: pd.DataFrame) -> DataArray: pred = self.models["pseudo_outcome"].predict(X)["posterior_predictive"].mu if self.cross_fitting: From f39b856a1e6311e6a404a23237b2691c56b46bb6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 15 Mar 2023 21:20:10 +0100 Subject: [PATCH 36/57] removed redundant _compute_cate function --- causalpy/skl_meta_learners.py | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 91966b12..acf774e3 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -430,8 +430,8 @@ class XLearner(SkMetaLearner): model : sklearn.base.RegressorMixin. If specified, it will be used in all of the subregressions, except for the propensity_score_model. Either model or all of treated_model, - untreated_model, treated_cate_estimator and untreated_cate_estimator have to - be specified. + untreated_model, treated_cate_estimator and untreated_cate_estimator have + to be specified. treated_model : sklearn.base.RegressorMixin. Model used for predicting target vector for treated values. untreated_model : sklearn.base.RegressorMixin. @@ -489,14 +489,7 @@ def __init__( self.fit(X, y, treated, coords=COORDS) # Compute cate - self.cate = self._compute_cate(X) - - def _compute_cate(self, X: pd.Series): - "Computes cate for given input." - cate_t = self.models["treated_cate"].predict(X) - cate_u = self.models["treated_cate"].predict(X) - g = self.models["propensity"].predict_proba(X)[:, 1] - return g * cate_u + (1 - g) * cate_t + self.cate = self.predict_cate(X) def fit( self, @@ -568,7 +561,7 @@ class DRLearner(SkMetaLearner): pseudo_outcome_model : sklearn.base.RegressorMixin Model used for pseudo-outcome estimation. propensity_score_model : sklearn.base.ClassifierMixin, - default = sklearn.linear_model.LogisticRegression(). + default = sklearn.linear_model.LogisticRegression(). Model used for propensity score estimation. cross_fitting : bool, default=False. If True, performs a cross fitting step. From 2ca0ebdd94fc65c4f74769e4a5104f1330bfac0f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 15 Mar 2023 21:38:40 +0100 Subject: [PATCH 37/57] formatting --- .../meta_learners_synthetic_data.ipynb | 89 +++++++++---------- 1 file changed, 42 insertions(+), 47 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index c12b0061..cb1136a9 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -55,24 +55,32 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "from causalpy.skl_meta_learners import SLearner, TLearner, XLearner, DRLearner\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.ensemble import HistGradientBoostingRegressor\n", - "from sklearn.dummy import DummyClassifier\n", - "from xgboost import XGBClassifier\n", "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", + "from sklearn.dummy import DummyClassifier\n", + "from sklearn.ensemble import (\n", + " AdaBoostRegressor,\n", + " HistGradientBoostingRegressor,\n", + " RandomForestRegressor,\n", + ")\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.svm import SVR\n", + "from xgboost import XGBClassifier, XGBRegressor\n", "\n", + "from causalpy.skl_meta_learners import DRLearner, SLearner, TLearner, XLearner\n", "\n", "np.random.seed(42)\n", "\n", + "\n", "def sigmoid(x):\n", " return np.exp(x) / (1 + np.exp(x))\n", "\n", + "\n", "def sinc(x):\n", - " return np.where(x==0, 1, np.sin(x) / x)\n", + " return np.where(x == 0, 1, np.sin(x) / x)\n", + "\n", "\n", "def create_synthetic_data(*shape):\n", " X = np.random.rand(*shape)\n", @@ -80,18 +88,19 @@ " α = np.random.rand(shape[1])\n", " β = np.random.rand(shape[1])\n", "\n", - " treatment = np.random.binomial(n=1, p=.25, size=shape[0])\n", + " treatment = np.random.binomial(n=1, p=0.25, size=shape[0])\n", " treatment_effect = sigmoid(X @ β)\n", "\n", " noise = np.random.rand(shape[0])\n", "\n", " y = sinc(X @ α) + treatment_effect * treatment + noise\n", "\n", - " X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(shape[1])])\n", - " y = pd.Series(y, name='target')\n", - " treated = pd.Series(treatment, name='treatment')\n", + " X = pd.DataFrame(X, columns=[f\"feature_{i}\" for i in range(shape[1])])\n", + " y = pd.Series(y, name=\"target\")\n", + " treated = pd.Series(treatment, name=\"treatment\")\n", " return X, y, treated, treatment_effect\n", "\n", + "\n", "X, y, treated, treatment_effect = create_synthetic_data(500, 20)" ] }, @@ -169,12 +178,7 @@ "source": [ "%%time\n", "# Utility for printing some statistics.\n", - "slearner = SLearner(\n", - " X=X,\n", - " y=y,\n", - " treated=treated,\n", - " model=HistGradientBoostingRegressor()\n", - ")\n", + "slearner = SLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", "summarize_learner(slearner)" ] @@ -225,12 +229,7 @@ ], "source": [ "%%time\n", - "tlearner = TLearner(\n", - " X=X,\n", - " y=y,\n", - " treated=treated,\n", - " model=HistGradientBoostingRegressor()\n", - ")\n", + "tlearner = TLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", "summarize_learner(tlearner)" ] @@ -279,12 +278,7 @@ "source": [ "%%time\n", "# Using logistic regression based propensity score\n", - "xlearner = XLearner(\n", - " X=X,\n", - " y=y,\n", - " treated=treated,\n", - " model=HistGradientBoostingRegressor()\n", - ")\n", + "xlearner = XLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", "summarize_learner(xlearner)" ] @@ -319,7 +313,7 @@ " y=y,\n", " treated=treated,\n", " model=HistGradientBoostingRegressor(),\n", - " propensity_score_model=DummyClassifier(strategy=\"most_frequent\")\n", + " propensity_score_model=DummyClassifier(strategy=\"most_frequent\"),\n", ")\n", "\n", "summarize_learner(xlearner_mf)" @@ -377,7 +371,7 @@ " y=y,\n", " treated=treated,\n", " model=HistGradientBoostingRegressor(),\n", - " propensity_score_model=DummyClassifier()\n", + " propensity_score_model=DummyClassifier(),\n", ")\n", "\n", "summarize_learner(drlearner)" @@ -413,7 +407,7 @@ " treated=treated,\n", " model=HistGradientBoostingRegressor(),\n", " propensity_score_model=DummyClassifier(),\n", - " cross_fitting=True\n", + " cross_fitting=True,\n", ")\n", "\n", "summarize_learner(drlearner)" @@ -428,21 +422,17 @@ }, "outputs": [], "source": [ - "from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor, AdaBoostRegressor\n", - "from lightgbm import LGBMRegressor\n", - "from sklearn.svm import SVR\n", - "from xgboost import XGBRegressor\n", - "\n", "def compute_mse(learner, m):\n", " l = learner(X, y, treated, m)\n", " return mean_squared_error(treatment_effect, l.cate)\n", "\n", + "\n", "models = [\n", " (\"Random forest\", RandomForestRegressor()),\n", " (\"Hist gradient boosting\", HistGradientBoostingRegressor()),\n", " (\"AdaBoost\", AdaBoostRegressor()),\n", " (\"XGBoost\", XGBRegressor()),\n", - " (\"SVR\", SVR())\n", + " (\"SVR\", SVR()),\n", "]\n", "\n", "learners = [\n", @@ -450,13 +440,16 @@ " (\"T-learner\", TLearner),\n", " (\"X-learner\", XLearner),\n", " (\"DR-learner\", DRLearner),\n", - " (\"Cross-fitted DR-learner\", lambda *args: DRLearner(*args, cross_fitting=True))\n", + " (\"Cross-fitted DR-learner\", lambda *args: DRLearner(*args, cross_fitting=True)),\n", "]\n", - "bench = pd.DataFrame({\n", - " m_name: {\n", - " learner_name: compute_mse(learner, m) for learner_name, learner in learners\n", - " } for m_name, m in models\n", - "})" + "bench = pd.DataFrame(\n", + " {\n", + " m_name: {\n", + " learner_name: compute_mse(learner, m) for learner_name, learner in learners\n", + " }\n", + " for m_name, m in models\n", + " }\n", + ")" ] }, { @@ -487,18 +480,20 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(10,10))\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", "sns.heatmap(bench, annot=True, ax=ax)" ] }, { - "cell_type": "raw", - "id": "0df81690-aba8-4f1c-9e2a-feb016664fca", + "attachments": {}, + "cell_type": "markdown", + "id": "57ed3040", "metadata": {}, "source": [ "Bibliography:\n", "\n", "[1] Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165.\n", + "\n", "[2] Kennedy, E. H. Optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint (2020) arXiv:2004.14497." ] } From 48c8105d3a235bf32598dec5ab4e5d5495b9c169 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 16 Mar 2023 17:10:48 +0100 Subject: [PATCH 38/57] added score method --- causalpy/skl_meta_learners.py | 88 +++++++++++++++++++++++++++++++++++ 1 file changed, 88 insertions(+) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index acf774e3..5f48c128 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -1,3 +1,4 @@ +"Scikit-learn based meta-learners." from copy import deepcopy from typing import Any, Dict @@ -259,6 +260,27 @@ def bias( return (bs_pred - pred).mean() + def score( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + ) -> Dict[str, np.float64]: + """ + Returns a dictionary of R^2 scores of base-learners, mean accuracy in case of + propensity score estimator. + + Parameters + ---------- + X : pandas.DataFrame of shape (_, n_features). + Data to predict on. + y : pandas.Series of shape (n_samples, ). + Target vector. + treated : pandas.Series of shape (n_samples, ). + Treatement assignment indicator consisting of zeros and ones. + """ + raise NotImplementedError() + def summary(self, n_iter: int = 1000) -> None: """ Prints summary. @@ -278,6 +300,8 @@ def summary(self, n_iter: int = 1000) -> None: print(f"Average treatement effect (ATE): {self.predict_ate(self.X)}") print(f"95% Confidence interval for ATE: {conf_ints}") print(f"Estimated bias: {bias}") + print("---- Score ----") + print(self.score(self.X, self.y, self.treated)) class SLearner(SkMetaLearner): @@ -329,6 +353,14 @@ def predict_cate(self, X: pd.DataFrame) -> pd.Series: m = self.models["model"] return pd.Series(m.predict(X_treated) - m.predict(X_control), index=self.index) + def score( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + ) -> Dict[str, np.float64]: + return {"model": self.models["model"].score(X.assign(treatment=treated), y)} + class TLearner(SkMetaLearner): """ @@ -409,6 +441,19 @@ def predict_cate(self, X: pd.DataFrame) -> pd.Series: treated_model.predict(X) - untreated_model.predict(X), index=self.index ) + def score( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + ) -> Dict[str, np.float64]: + X_t, y_t = X[treated == 1], y[treated == 1] + X_u, y_u = X[treated == 0], y[treated == 0] + return { + "treated": self.models["treated"].score(X_t, y_t), + "untreated": self.models["untreated"].score(X_u, y_u), + } + class XLearner(SkMetaLearner): """ @@ -531,6 +576,26 @@ def predict_cate(self, X: pd.DataFrame) -> pd.Series: cate = g * cate_estimate_untreated + (1 - g) * cate_estimate_treated return pd.Series(cate, index=self.index) + def score( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + ) -> Dict[str, np.float64]: + X_t, y_t = X[treated == 1], y[treated == 1] + X_u, y_u = X[treated == 0], y[treated == 0] + + tau_t = y_t - self.models["untreated"].predict(X_t) + tau_u = self.models["treated"].predict(X_u) - y_u + + return { + "treated": self.models["treated"].score(X_t, y_t), + "untreated": self.models["untreated"].score(X_u, y_u), + "propensity": self.models["propensity"].score(X, treated), + "treated_cate": self.models["treated_cate"].score(X_t, tau_t), + "untreated_cate": self.models["untreated_cate"].score(X_u, tau_u), + } + class DRLearner(SkMetaLearner): """ @@ -686,3 +751,26 @@ def predict_cate(self, X: pd.DataFrame) -> pd.Series: pred = (pred + pred2) / 2 return pd.Series(pred, index=self.index) + + def score( + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + ) -> Dict[str, np.float64]: + X_t, y_t = X[treated == 1], y[treated == 1] + X_u, y_u = X[treated == 0], y[treated == 0] + + g = self.models["propensity"].predict_proba(X)[:, 1] + mu_0 = self.models["untreated"].predict(X) + mu_1 = self.models["treated"].predict(X) + mu_w = np.where(treated == 0, mu_0, mu_1) + + pseudo_outcome = (treated - g) / (g * (1 - g)) * (y - mu_w) + mu_1 - mu_0 + + return { + "treated": self.models["treated"].score(X_t, y_t), + "untreated": self.models["untreated"].score(X_u, y_u), + "propensity": self.models["propensity"].score(X, treated), + "pseudo-outcome": self.models["pseudo_outcome"].score(X, pseudo_outcome), + } From ddaebb45a08f979fc55cd3ebc228612f6c1c8d8c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 16 Mar 2023 17:23:44 +0100 Subject: [PATCH 39/57] formatting --- causalpy/pymc_meta_learners.py | 112 +++++++++++++++------------------ 1 file changed, 52 insertions(+), 60 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index 92b7dfaa..7e9b3353 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -1,3 +1,4 @@ +"PyMC based meta-learners." from typing import Any, Dict, Optional import arviz as az @@ -6,7 +7,6 @@ from sklearn.model_selection import train_test_split from xarray.core.dataarray import DataArray - from causalpy.pymc_models import LogisticRegression, ModelBuilder from causalpy.skl_meta_learners import ( DRLearner, @@ -42,7 +42,7 @@ def fit( Dictionary containing the keys coeffs and obs_indx. """ raise NotImplementedError() - + def predict_cate(self, X: pd.DataFrame) -> DataArray: """ Predicts distribution of treatement effect. @@ -145,7 +145,7 @@ class BayesianXLearner(XLearner, BayesianMetaLearner): untreated_model, treated_cate_estimator and untreated_cate_estimator have to be specified. treated_model : causalpy.pymc_models.ModelBuilder. - Model used for predicting target vector for treated values. + Model used for predicting target vector for treated values. untreated_model : causalpy.pymc_models.ModelBuilder. Model used for predicting target vector for untreated values. untreated_cate_estimator : causalpy.pymc_models.ModelBuilder. @@ -154,32 +154,33 @@ class BayesianXLearner(XLearner, BayesianMetaLearner): Model used for CATE estimation on treated data. propensity_score_model : causalpy.pymc_models.ModelBuilder, default = causalpy.pymc_models.LogisticRegression(). - Model used for propensity score estimation. Output values should be in the + Model used for propensity score estimation. Output values should be in the interval [0, 1]. """ def __init__( - self, - X: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - model: Optional[ModelBuilder] = None, - treated_model: Optional[ModelBuilder] = None, - untreated_model: Optional[ModelBuilder] = None, - treated_cate_estimator: Optional[ModelBuilder] = None, - untreated_cate_estimator: Optional[ModelBuilder] = None, - propensity_score_model: Optional[ModelBuilder] = None, - ): + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + model: Optional[ModelBuilder] = None, + treated_model: Optional[ModelBuilder] = None, + untreated_model: Optional[ModelBuilder] = None, + treated_cate_estimator: Optional[ModelBuilder] = None, + untreated_cate_estimator: Optional[ModelBuilder] = None, + propensity_score_model: Optional[ModelBuilder] = None, + ): super().__init__( X, y, - treated, model, + treated, + model, treated_model, untreated_model, treated_cate_estimator, untreated_cate_estimator, propensity_score_model, - ) + ) def fit( self, @@ -203,14 +204,10 @@ def fit( _fit(untreated_model, X_u, y_u, coords) pred_u_t = az.extract( - untreated_model.predict(X_t), - group="posterior_predictive", - var_names="mu" + untreated_model.predict(X_t), group="posterior_predictive", var_names="mu" ).mean(axis=1) pred_t_u = az.extract( - treated_model.predict(X_u), - group="posterior_predictive", - var_names="mu" + treated_model.predict(X_u), group="posterior_predictive", var_names="mu" ).mean(axis=1) tau_t = y_t - pred_u_t @@ -258,7 +255,7 @@ class BayesianDRLearner(DRLearner, BayesianMetaLearner): untreated_model, treated_cate_estimator and untreated_cate_estimator have to be specified. treated_model : causalpy.pymc_models.ModelBuilder. - Model used for predicting target vector for treated values. + Model used for predicting target vector for treated values. untreated_model : causalpy.pymc_models.ModelBuilder. Model used for predicting target vector for untreated values. pseudo_outcome_model : causalpy.pymc_models.ModelBuilder. @@ -281,32 +278,30 @@ def __init__( pseudo_outcome_model: Optional[ModelBuilder] = None, propensity_score_model: Optional[ModelBuilder] = LogisticRegression(), cross_fitting: bool = False, - ): + ): super().__init__( - X, - y, - treated, - model, - treated_model, - untreated_model, - pseudo_outcome_model, - propensity_score_model, - cross_fitting, + X, + y, + treated, + model, + treated_model, + untreated_model, + pseudo_outcome_model, + propensity_score_model, + cross_fitting, ) def fit( - self, - X: pd.DataFrame, - y: pd.Series, - treated: pd.Series, - coords: Dict[str, Any] = None, + self, + X: pd.DataFrame, + y: pd.Series, + treated: pd.Series, + coords: Dict[str, Any] = None, ): # Split data to two independent samples of equal size - ( - X0, X1, - y0, y1, - treated0, treated1 - ) = train_test_split(X, y, treated, stratify=treated, test_size=.5) + (X0, X1, y0, y1, treated0, treated1) = train_test_split( + X, y, treated, stratify=treated, test_size=0.5 + ) treated_model = self.models["treated"] untreated_model = self.models["untreated"] @@ -326,26 +321,23 @@ def fit( # Fit propensity score model _fit(propensity_score_model, X0, treated0, coords) - + g = az.extract( propensity_score_model.predict(X1), group="posterior_predictive", - var_names="mu").mean(axis=1) + var_names="mu", + ).mean(axis=1) mu_0 = az.extract( untreated_model.predict(X1), group="posterior_predictive", - var_names="mu").mean(axis=1) + var_names="mu", + ).mean(axis=1) mu_1 = az.extract( - treated_model.predict(X1), - group="posterior_predictive", - var_names="mu").mean(axis=1) - + treated_model.predict(X1), group="posterior_predictive", var_names="mu" + ).mean(axis=1) mu_w = np.where(treated1 == 0, mu_0, mu_1) - pseudo_outcome = ( - (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 - ) - + pseudo_outcome = (treated1 - g) / (g * (1 - g)) * (y1 - mu_w) + mu_1 - mu_0 # Fit pseudo-outcome model _fit(pseudo_outcome_model, X1, pseudo_outcome, coords) @@ -360,7 +352,7 @@ def fit( untreated_model = self.cross_fitted_models["untreated"] propensity_score_model = self.cross_fitted_models["propensity"] pseudo_outcome_model = self.cross_fitted_models["pseudo_outcome"] - + second_iteration = True else: return self @@ -373,9 +365,9 @@ def predict_cate(self, X: pd.DataFrame) -> DataArray: if self.cross_fitting: pred2 = ( self.cross_fitted_models["pseudo_outcome"] - .predict(X) - ["posterior_predictive"] - .mu) + .predict(X)["posterior_predictive"] + .mu + ) pred = (pred + pred2) / 2 - return pred \ No newline at end of file + return pred From 3bb16fe7fe48b5be462e57dd22ceb91508426953 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 16 Mar 2023 17:33:55 +0100 Subject: [PATCH 40/57] reworded introduction + included some suggestions by @juanitorduz --- .../meta_learners_synthetic_data.ipynb | 188 ++++++++++-------- 1 file changed, 106 insertions(+), 82 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index cb1136a9..b87e9014 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -15,13 +15,30 @@ "source": [ "## Introduction\n", "\n", - "Meta-learners are a class of models used for estimating *heterogeneous causal effect* of a certain treatement. Here by heterogeneity, we mean that the effect of the treatement may vary across individuals. They approach this problem by decomposing it to several subregressions. The models used for these subregressions are called *base learners*. Some of the most popular base learners are tree based ensembles, in particular Random Forest and Bayesian Additive Regression Trees (BART) due to their flexibility, however any machine learning algorithm suitable for regression problems (like generalized linear regressions, gaussian processes, etc.) can be used.\n", + "When estimating causal effect of a certain treatment, generally we cannot assume that all of the treated units will respond to the treatement in the same way, that is the causal effect is might be *heterogeneous*. For example, the effectiveness of a certain medication might depend on the patient's age, gender, medical history, etc.. Estimating the treatement effect conditional to a set of covariates can provide valuable insights into our data. In this notebook we give a brief introduction to *meta-learners*, a class of models for estimating heterogeneous causal effect by leveraging machine learning methods.\n", "\n", - "We will denote by $Y$ a $d$-dimensional outcome vector, by $X \\in \\mathbb{R}^{d \\times n}$ a feature matrix containing potential confounders, by $W \\in \\{0, 1\\}^d$ the treatment assignment indicator and by $Y(0) \\in \\mathbb{R}^d$ and $Y(1) \\in \\mathbb{R}^d$ the potential outcome of corresponding units when assigned to the control and the treatement group respectively. Note that in general, we only observe one of the potential outcomes, furthermore\n", + "For a deeper dive on meta-learners we suggest reading [[1]](https://arxiv.org/abs/1706.03461) or for a lighter overview in video format, check out [Causal Effects via Regression by Shawhin Talebi](https://youtu.be/O72uByJlnMw). We also highly recommend reading Causal Inference for The Brave and True by Matheus Facure Alves [[3]](https://matheusfacure.github.io/python-causality-handbook/landing-page.html) for an easily digestible, yet comprehensive guide on several estimators discussed in this notebook.\n", + "\n", + "We will denote by $Y$ a $d$-dimensional outcome vector, by $X \\in \\mathbb{R}^{d \\times n}$ a feature matrix containing potential confounders, by $W \\in \\{0, 1\\}^d$ the treatment assignment indicator and by $Y(0) \\in \\mathbb{R}^d$ and $Y(1) \\in \\mathbb{R}^d$ the potential outcome of corresponding units when assigned to the control and the treatement group respectively. Note that\n", "$$ Y = W Y(1) + (1 - W) Y(0).$$\n", "\n", "Our task is to estimate the *conditional average treatement effect (CATE)*, defined as \n", - "$$\\tau(x) = \\mathbb{E}\\left[Y(1) - Y(0) \\mid X=x\\right].$$" + "$$\\tau(x) = \\mathbb{E}\\left[Y(1) - Y(0) \\mid X=x\\right].$$\n", + "\n", + "Since only one of the potential outcomes materializes, CATE is never directly observed, this is often refered to as *the fundamental problem of causal inference*. Meta-learners estimate CATE by decomposing the treatement effect estimation to several subregressions. For instance the one may estimate $Y(0)$ and $Y(1)$ in two separate regressions and estimate CATE as the difference of the two functions (this is the approach of the T-learner discussed later). The estimators used for these subregressions are called *base learners*. Some of the most popular base learners are tree based ensembles, in particular Random Forest and Bayesian Additive Regression Trees (BART) due to their flexibility, however any machine learning algorithm suitable for regression problems (like generalized linear regressions, gaussian processes, etc.) can be used." + ] + }, + { + "cell_type": "markdown", + "id": "c06012dc-2adc-40df-bdec-6de7d884396e", + "metadata": {}, + "source": [ + "## Assumptions\n", + "As always, in order to be able to derive meaningful results, we need to impose some untestable assumptions. \n", + "1. __(Consistency)__: If the $i$th unit is assigned to the treated group, in other words $W_i=1$, then $Y_i(1)$ is observed and $Y_i(0)$ is observed otherwise.\n", + "2. __(Unconfoundedness)__: The treatement assignment $W_i$ is independent from $Y_i(0)$ and $Y_i(1)$ conditional on $X_i$, that is\n", + "$$Y_i(0), Y_i(1) \\perp\\!\\!\\!\\perp W_i\\mid X_i.$$\n", + "3. __(Overlap)__: $0 < \\mathbb{P}(W_i = 1 \\mid X_i=x) < 1$, i.e. treatement assignment is non-deterministic." ] }, { @@ -101,7 +118,7 @@ " return X, y, treated, treatment_effect\n", "\n", "\n", - "X, y, treated, treatment_effect = create_synthetic_data(500, 20)" + "X, y, treated, treatment_effect = create_synthetic_data(100, 5)" ] }, { @@ -109,20 +126,21 @@ "id": "19940b36-b376-4e2c-9899-91a5162319c8", "metadata": {}, "source": [ - "## Summary\n", + "## Model evaluation\n", "The summary method of a scikit-learn based meta-learner contains\n", "* Number of observations,\n", "* number of treated observations,\n", "* average treatement effect (ATE),\n", "* 95% Confidence interval for ATE,\n", - "* estimated bias.\n", + "* estimated bias,\n", + "* in-sample $R^2$. \n", "\n", "Confidence intervals and bias are calculated when summary is called via bootstrapping. This means that the runtime of each learner in these examples will be exaggerated. " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "8dc5e4d6-c632-4ca2-85e6-2d9a7aae892f", "metadata": {}, "outputs": [], @@ -155,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "454322d0-6dbc-4771-bb7a-344afa30e793", "metadata": {}, "outputs": [ @@ -163,15 +181,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 126\n", - "Average treatement effect (ATE): 1.0158653426134698\n", - "95% Confidence interval for ATE: (0.7168756519845457, 1.4142990694909983)\n", - "Estimated bias: -0.0032205093538485788\n", - "Actual average treatement effect: 0.9935910373038435\n", - "MSE: 0.04298096898757659\n", - "CPU times: total: 6min 23s\n", - "Wall time: 55.9 s\n" + "Number of observations: 100\n", + "Number of treated observations: 26\n", + "Average treatement effect (ATE): 0.7573144867185623\n", + "95% Confidence interval for ATE: (0.6551374637321661, 1.1244220824288043)\n", + "Estimated bias: -0.015414836797426407\n", + "---- Score ----\n", + "{'model': 0.7407260838009135}\n", + "Actual average treatement effect: 0.8455644128466571\n", + "MSE: 0.012366285859837036\n", + "CPU times: total: 2min 16s\n", + "Wall time: 22.2 s\n" ] } ], @@ -207,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "2a24a76e-398a-4629-9d49-7fadecb2a2c7", "metadata": {}, "outputs": [ @@ -215,15 +235,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 126\n", - "Average treatement effect (ATE): 1.0602041004116796\n", - "95% Confidence interval for ATE: (0.6678309683577215, 1.5429661284355667)\n", - "Estimated bias: 0.009071047889501997\n", - "Actual average treatement effect: 0.9935910373038435\n", - "MSE: 0.08202638716051415\n", - "CPU times: total: 6min 32s\n", - "Wall time: 55.4 s\n" + "Number of observations: 100\n", + "Number of treated observations: 26\n", + "Average treatement effect (ATE): 0.9696548539528407\n", + "95% Confidence interval for ATE: (0.6260569983725455, 1.380766593414531)\n", + "Estimated bias: 0.0018099847967227778\n", + "---- Score ----\n", + "{'treated': -0.05211016280333158, 'untreated': 0.4544960064501088}\n", + "Actual average treatement effect: 0.8455644128466571\n", + "MSE: 0.034462462131719725\n", + "CPU times: total: 2min 30s\n", + "Wall time: 24.7 s\n" ] } ], @@ -240,6 +262,7 @@ "metadata": {}, "source": [ "# X-learner\n", + "The X-learner was first introduced in [[1]](https://arxiv.org/abs/1706.03461) by\n", "Similarly, to the T-learner, the X-learner first estimates $\\mu_{\\operatorname{treated}}$ and $\\mu_{\\operatorname{untreated}}$. Next, it computes \n", "\n", "$$D(1) := Y(1) - \\hat{\\mu}_{\\operatorname{untreated}}(X(1)), \\quad \\text{and} \\quad D(0) = \\hat{\\mu}_{\\operatorname{treated}}(X(0)) - Y(0).$$\n", @@ -255,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "250bd516-b1ad-452f-b26c-e0eafc3747f8", "metadata": {}, "outputs": [ @@ -263,15 +286,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 126\n", - "Average treatement effect (ATE): 1.0241946147950118\n", - "95% Confidence interval for ATE: (0.7780033378922033, 1.3792194001196363)\n", - "Estimated bias: -0.0030826969031921064\n", - "Actual average treatement effect: 0.9935910373038435\n", - "MSE: 0.04195048793085756\n", - "CPU times: total: 13min 10s\n", - "Wall time: 1min 52s\n" + "Number of observations: 100\n", + "Number of treated observations: 26\n", + "Average treatement effect (ATE): 0.8837881368411192\n", + "95% Confidence interval for ATE: (0.7604194149047154, 1.0213360451924018)\n", + "Estimated bias: -0.015952518856480535\n", + "---- Score ----\n", + "{'treated': -0.022718608616440372, 'untreated': 0.4615650975283646, 'propensity': 0.74, 'treated_cate': -0.01508932784396233, 'untreated_cate': 0.46156509752836483}\n", + "Actual average treatement effect: 0.8455644128466571\n", + "MSE: 0.0035833820875945765\n", + "CPU times: total: 4min 13s\n", + "Wall time: 39.5 s\n" ] } ], @@ -285,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "aaab018b-c95e-4c29-9163-410e39cd34de", "metadata": {}, "outputs": [ @@ -293,15 +318,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 126\n", - "Average treatement effect (ATE): 1.036517292248574\n", - "95% Confidence interval for ATE: (0.7821662021543369, 1.3970869383992666)\n", - "Estimated bias: 0.0016462894459864125\n", - "Actual average treatement effect: 0.9935910373038435\n", - "MSE: 0.04195048793085756\n", - "CPU times: total: 13min 5s\n", - "Wall time: 1min 48s\n" + "Number of observations: 100\n", + "Number of treated observations: 26\n", + "Average treatement effect (ATE): 0.8615441480227912\n", + "95% Confidence interval for ATE: (0.8619040475980911, 0.9780900167241081)\n", + "Estimated bias: -0.016991817144321627\n", + "---- Score ----\n", + "{'treated': -0.07111924089890742, 'untreated': 0.5176039158889738, 'propensity': 0.74, 'treated_cate': -0.052085096064813374, 'untreated_cate': 0.517603915888974}\n", + "Actual average treatement effect: 0.8455644128466571\n", + "MSE: 0.004913496827435016\n", + "CPU times: total: 4min 5s\n", + "Wall time: 39.5 s\n" ] } ], @@ -319,14 +346,6 @@ "summarize_learner(xlearner_mf)" ] }, - { - "cell_type": "markdown", - "id": "adf8263c-1ceb-4ee8-9302-69961892214f", - "metadata": {}, - "source": [ - "For a deeper dive on the aforementioned three estimators I suggest reading [1] or for a lighter overview in video format, check out [Causal Effects via Regression by Shawhin Talebi](https://youtu.be/O72uByJlnMw)." - ] - }, { "cell_type": "markdown", "id": "6a499cc5-2fcf-4016-978b-c3c28c861d5a", @@ -334,7 +353,7 @@ "source": [ "# DR-learner\n", "\n", - "The *doubly-robust learner*, also known as the *DR-learner*, takes its name from the property that it is unbiased if either the propensity score estimator or the outcome regressions are correctly specified.\n", + "The *doubly-robust learner*, also known as the *DR-learner*, takes its name from the property that it is unbiased if either the propensity score estimator or the outcome regressions are correctly specified and it was first introduced in [[2]](https://arxiv.org/abs/2004.14497).\n", "\n", "The DR-learner first splits the set of observations $S = (Y, X, W)$ randomly into two parts $S_i = (Y_i, X_i, W_i), i=1,2$ of equally many observations, finds the estimates $\\hat{\\mu}_{\\operatorname{treated}}$, $\\hat{\\mu}_{\\operatorname{untreated}}$ and $g$ on $S_1$. Then it constructs \n", "\n", @@ -344,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "687aa584-b8af-4863-a7cf-570e86080057", "metadata": {}, "outputs": [ @@ -352,15 +371,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 126\n", - "Average treatement effect (ATE): 1.043603596223281\n", - "95% Confidence interval for ATE: (0.5722356606341764, 1.791826637224789)\n", - "Estimated bias: 0.06476430795642821\n", - "Actual average treatement effect: 0.9935910373038435\n", - "MSE: 0.26413051181872693\n", - "CPU times: total: 7min 7s\n", - "Wall time: 58.8 s\n" + "Number of observations: 100\n", + "Number of treated observations: 26\n", + "Average treatement effect (ATE): 0.9172354454463756\n", + "95% Confidence interval for ATE: (0.691107742990425, 1.5065847599391242)\n", + "Estimated bias: 0.03131311626014071\n", + "---- Score ----\n", + "{'treated': -0.009335753981434936, 'untreated': -0.0006342347399423964, 'propensity': 0.74, 'pseudo-outcome': 0.015108882815612734}\n", + "Actual average treatement effect: 0.8455644128466571\n", + "MSE: 0.04738912217493377\n", + "CPU times: total: 3min 1s\n", + "Wall time: 31.2 s\n" ] } ], @@ -379,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "ea54609b-9f4f-4282-b60c-498de1af106b", "metadata": {}, "outputs": [ @@ -387,15 +408,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 500\n", - "Number of treated observations: 126\n", - "Average treatement effect (ATE): 1.0124610535982221\n", - "95% Confidence interval for ATE: (0.5464324003175869, 1.6467470784122396)\n", - "Estimated bias: -0.012888295172939901\n", - "Actual average treatement effect: 0.9935910373038435\n", - "MSE: 0.11390230987800598\n", - "CPU times: total: 14min 6s\n", - "Wall time: 1min 56s\n" + "Number of observations: 100\n", + "Number of treated observations: 26\n", + "Average treatement effect (ATE): 0.9422325586455574\n", + "95% Confidence interval for ATE: (0.6719756565007992, 1.3349122961116073)\n", + "Estimated bias: -0.03275843981803161\n", + "---- Score ----\n", + "{'treated': -7.045901399438392e-05, 'untreated': -0.07354364375362898, 'propensity': 0.74, 'pseudo-outcome': -0.003368392413228616}\n", + "Actual average treatement effect: 0.8455644128466571\n", + "MSE: 0.03459741914128395\n", + "CPU times: total: 6min 25s\n", + "Wall time: 1min 19s\n" ] } ], @@ -415,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "3552a561-8051-4a51-832a-5e088a106689", "metadata": { "tags": [] @@ -454,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "7eed6094-c940-4b5b-8294-bd2ffeab2eac", "metadata": {}, "outputs": [ @@ -464,13 +487,13 @@ "" ] }, - "execution_count": 6, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { 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", 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" ] @@ -485,7 +508,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "57ed3040", "metadata": {}, @@ -494,7 +516,9 @@ "\n", "[1] Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences 116, no. 10 (2019): 4156-4165.\n", "\n", - "[2] Kennedy, E. H. Optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint (2020) arXiv:2004.14497." + "[2] Kennedy, E. H. Optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint (2020) arXiv:2004.14497.\n", + "\n", + "[3] Alves, M. F. Causal Inference for The Brave and True. [https://matheusfacure.github.io/python-causality-handbook](https://matheusfacure.github.io/python-causality-handbook)." ] } ], From 0d98c53ae00ccc2ffbca6bb7696b3876c3839833 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Thu, 16 Mar 2023 17:35:23 +0100 Subject: [PATCH 41/57] minor changes --- causalpy/pymc_models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 140d6439..f502f4f6 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -1,3 +1,4 @@ +"PyMC based meta-learners." from typing import Any, Dict, Optional import arviz as az From 02b78e165f9ca4fa300694410c37c20644144f16 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Fri, 17 Mar 2023 10:36:23 +0100 Subject: [PATCH 42/57] formatting --- causalpy/utils.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/causalpy/utils.py b/causalpy/utils.py index bbc02fdd..92df3574 100644 --- a/causalpy/utils.py +++ b/causalpy/utils.py @@ -4,8 +4,10 @@ def _fit(model, X, y, coords): - """Fits model to X, y, where model is either a sklearn model or a ModelBuilder - instance. In the later case it passes coords, in the first case coords is ignored.""" + """ + Fits model to X, y, where model is either a sklearn model or a ModelBuilder + instance. In the later case it passes coords, in the first case coords is ignored. + """ if isinstance(model, ModelBuilder): model.fit(X, y, coords) else: From 3e845bfa15dce83196befa90250a1b315cf850a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Fri, 17 Mar 2023 14:55:40 +0100 Subject: [PATCH 43/57] added correct docstring --- causalpy/pymc_models.py | 1 - 1 file changed, 1 deletion(-) diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index f502f4f6..140d6439 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -1,4 +1,3 @@ -"PyMC based meta-learners." from typing import Any, Dict, Optional import arviz as az From d4830cc990ff65182cbaa249891fbf4e773397a2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 22 Mar 2023 10:21:13 +0100 Subject: [PATCH 44/57] added aesera to list of dependencies --- .../meta_learners_synthetic_data.ipynb | 92 ++++++++++++++++--- pyproject.toml | 5 +- 2 files changed, 81 insertions(+), 16 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index b87e9014..09c2e0be 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -38,7 +38,10 @@ "1. __(Consistency)__: If the $i$th unit is assigned to the treated group, in other words $W_i=1$, then $Y_i(1)$ is observed and $Y_i(0)$ is observed otherwise.\n", "2. __(Unconfoundedness)__: The treatement assignment $W_i$ is independent from $Y_i(0)$ and $Y_i(1)$ conditional on $X_i$, that is\n", "$$Y_i(0), Y_i(1) \\perp\\!\\!\\!\\perp W_i\\mid X_i.$$\n", - "3. __(Overlap)__: $0 < \\mathbb{P}(W_i = 1 \\mid X_i=x) < 1$, i.e. treatement assignment is non-deterministic." + "3. __(Overlap)__: $0 < \\mathbb{P}(W_i = 1 \\mid X_i=x) < 1$, i.e. treatement assignment is non-deterministic.\n", + "\n", + "Given these assumptions, $\\tau(x)$ can be written as the difference of the expected values observed random variables, namely\n", + "$$\\tau(x) = \\mathbb{E}[Y(1) \\mid X=x, W=1] - \\mathbb{E}[Y(0) \\mid X=x, W=0].$$" ] }, { @@ -148,7 +151,8 @@ "def summarize_learner(learner):\n", " learner.summary(n_iter=100)\n", " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", - " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")" + " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")\n", + " learner.average_uplift_by_percentile(X).plot()" ] }, { @@ -190,9 +194,19 @@ "{'model': 0.7407260838009135}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.012366285859837036\n", - "CPU times: total: 2min 16s\n", - "Wall time: 22.2 s\n" + "CPU times: total: 1min 26s\n", + "Wall time: 11.5 s\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -244,9 +258,19 @@ "{'treated': -0.05211016280333158, 'untreated': 0.4544960064501088}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.034462462131719725\n", - "CPU times: total: 2min 30s\n", - "Wall time: 24.7 s\n" + "CPU times: total: 1min 47s\n", + "Wall time: 14 s\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -295,9 +319,19 @@ "{'treated': -0.022718608616440372, 'untreated': 0.4615650975283646, 'propensity': 0.74, 'treated_cate': -0.01508932784396233, 'untreated_cate': 0.46156509752836483}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.0035833820875945765\n", - "CPU times: total: 4min 13s\n", - "Wall time: 39.5 s\n" + "CPU times: total: 3min 38s\n", + "Wall time: 29 s\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -327,9 +361,19 @@ "{'treated': -0.07111924089890742, 'untreated': 0.5176039158889738, 'propensity': 0.74, 'treated_cate': -0.052085096064813374, 'untreated_cate': 0.517603915888974}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.004913496827435016\n", - "CPU times: total: 4min 5s\n", - "Wall time: 39.5 s\n" + "CPU times: total: 3min 28s\n", + "Wall time: 27.5 s\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -380,9 +424,19 @@ "{'treated': -0.009335753981434936, 'untreated': -0.0006342347399423964, 'propensity': 0.74, 'pseudo-outcome': 0.015108882815612734}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.04738912217493377\n", - "CPU times: total: 3min 1s\n", - "Wall time: 31.2 s\n" + "CPU times: total: 2min 13s\n", + "Wall time: 17.5 s\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -417,9 +471,19 @@ "{'treated': -7.045901399438392e-05, 'untreated': -0.07354364375362898, 'propensity': 0.74, 'pseudo-outcome': -0.003368392413228616}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.03459741914128395\n", - "CPU times: total: 6min 25s\n", - "Wall time: 1min 19s\n" + "CPU times: total: 4min 21s\n", + "Wall time: 35.9 s\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ diff --git a/pyproject.toml b/pyproject.toml index 9e1d002a..0b4f7fe3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ requires-python = ">=3.8" # For an analysis of this field vs pip's requirements files see: # https://packaging.python.org/discussions/install-requires-vs-requirements/ dependencies = [ + "aesera", "arviz>=0.14.0", "graphviz", "ipython!=8.7.0", @@ -33,11 +34,11 @@ dependencies = [ "pandas", "patsy", "pymc>=5.0.0", + "pymc_bart", "scikit-learn>=1", "scipy", "seaborn>=0.11.2", - "xarray>=v2022.11.0", - "pymc_bart" + "xarray>=v2022.11.0" ] # List additional groups of dependencies here (e.g. development dependencies). Users From 02d592c0628396109c22dd8285ce54fccc5f3234 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 27 Mar 2023 16:27:53 +0200 Subject: [PATCH 45/57] improved docstrings. --- causalpy/pymc_models.py | 28 ++++++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 140d6439..9cc6b843 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -117,10 +117,26 @@ def build_model(self, X, y, coords): pm.Normal("y_hat", mu, sigma, observed=y, dims="obs_ind") -class BARTModel(ModelBuilder): - "Class for building BART based models for meta-learners." +class BARTRegressor(ModelBuilder): + """ + Class for building BART based models for meta-learners. + + Parameters + ---------- + m : int. + Number of trees to fit. + sigma : float. + Prior standard deviation. + sample_kwargs : dict. + Keyword arguments for sampler. + """ - def __init__(self, sample_kwargs=None, m=20, sigma=1): + def __init__( + self, + m: int = 20, + sigma: float = 1.0, + sample_kwargs: Optional[dict[str, Any]] = None, + ): self.m = m self.sigma = sigma super().__init__(sample_kwargs) @@ -141,9 +157,9 @@ class LogisticRegression(ModelBuilder): ---------- coeff_distribution : PyMC distribution. Prior distribution of coefficient vector. - distribution_kwargs + distribution_kwargs : dict. Keyword arguments for prior distribution. - sample_kwargs + sample_kwargs : dict. Keyword arguments for sampler. Examples @@ -164,7 +180,7 @@ class LogisticRegression(ModelBuilder): def __init__( self, - sample_kwargs=None, + sample_kwargs: Optional[dict[str, Any]] = None, coeff_distribution: DistributionMeta = pm.Normal, coeff_distribution_kwargs: Optional[dict[str, Any]] = None, ): From 2007685bb063d518e9ccc4a50ef14458e27b382d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 27 Mar 2023 17:13:45 +0200 Subject: [PATCH 46/57] XLearner computations were wrong --- causalpy/pymc_meta_learners.py | 4 ++-- causalpy/pymc_models.py | 31 ++++++++++++++++++++++++++++++- 2 files changed, 32 insertions(+), 3 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index 7e9b3353..742046a4 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -211,7 +211,7 @@ def fit( ).mean(axis=1) tau_t = y_t - pred_u_t - tau_u = y_u - pred_t_u + tau_u = - y_u + pred_t_u # Estimate CATE separately on treated and untreated subsets _fit(treated_cate_estimator, X_t, tau_t, coords) @@ -229,7 +229,7 @@ def predict_cate(self, X: pd.DataFrame) -> DataArray: cate_estimate_untreated = untreated_model.predict(X)["posterior_predictive"].mu g = self.models["propensity"].predict(X)["posterior_predictive"].mu - return g * cate_estimate_untreated + (1 - g) * cate_estimate_treated + return (1 - g) * cate_estimate_untreated + g * cate_estimate_treated class BayesianDRLearner(DRLearner, BayesianMetaLearner): diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 9cc6b843..ffd847e7 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -119,7 +119,7 @@ def build_model(self, X, y, coords): class BARTRegressor(ModelBuilder): """ - Class for building BART based models for meta-learners. + Class for building BART based regressors for meta-learners. Parameters ---------- @@ -149,6 +149,35 @@ def build_model(self, X, y, coords=None): pm.Normal("y_hat", mu=mu, sigma=self.sigma, observed=y, dims="obs_ind") +class BARTClassifier(ModelBuilder): + """ + Class for building BART based models for meta-learners. + + Parameters + ---------- + m : int. + Number of trees to fit. + sample_kwargs : dict. + Keyword arguments for sampler. + """ + + def __init__( + self, + m: int = 20, + sample_kwargs: Optional[dict[str, Any]] = None, + ): + self.m = m + super().__init__(sample_kwargs) + + def build_model(self, X, y, coords=None): + with self: + self.add_coords(coords) + X_ = pm.MutableData("X", X, dims=["obs_ind", "coeffs"]) + mu_ = pmb.BART("mu_", X_, y, m=self.m, dims="obs_ind") + mu = pm.Deterministic("mu", pm.math.sigmoid(mu_), dims="obs_ind") + pm.Bernoulli("y_hat", mu, observed=y, dims="obs_ind") + + class LogisticRegression(ModelBuilder): """ Custom PyMC model for logistic regression. From a9363069b9c0b3a1611253b8da21f4ac7757cb4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 29 Mar 2023 16:35:43 +0200 Subject: [PATCH 47/57] added summary file --- causalpy/summary.py | 208 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 208 insertions(+) create mode 100644 causalpy/summary.py diff --git a/causalpy/summary.py b/causalpy/summary.py new file mode 100644 index 00000000..284a0eb3 --- /dev/null +++ b/causalpy/summary.py @@ -0,0 +1,208 @@ +"Summary objects." + +from dataclasses import dataclass +from typing import Optional + + +def html_header(columns: list[str], colspan: int = 1): + """ + Returns HTML code for table header. + + Parameters + ---------- + columns : list[str]. + List containing column names. + colspan : int. + Column span. + """ + string = ( + f' ' + + (f' '.join(columns)) + + "" + ) + return '' + string + "" + + +def html_rows(index: str, values: list, colspan: int = 1): + """ + Returns HTML code for table rows. + + Parameters + ---------- + items : dict. + The keys of items is the index-set of the rows to be added. The values are + lists containing the values to be added. + colspan : int. + Column span. + """ + string = "" + string += f' {index} ' + + if not isinstance(values, list): + values = [values] + + values = map(str, values) + string += ( + f'' + + (' '.join(values)) + + "" + ) + + string = f" {string} " + + return string + + +def str_header(col_names, length): + # If first column is empty, it's box should not be displayed + first_col_empty = col_names[0] == "" + + if first_col_empty: + col_names = col_names[1:] + + n_cols = len(col_names) + + top = f"{length * '═'}╦" + bot = f"{length * '═'}╩" + + spaces = first_col_empty * (length + 1) * " " + + return f"""\ + {spaces}╔{(n_cols - 1) * top}{length * "═"}╗ + {spaces}║{"║".join(map(lambda x: x.center(length), col_names))}║ + ┏{length * '━'}╚{(n_cols - 1) * bot}{length * "═"}╝\ + """ + + +def str_row(index, values, length, row_type="inner"): + n_cols = len(values) + values = map(str, values) + + s = "" + + if row_type == "first": + s += f""" ┏{length * '━'}┱""" + s += f"{(n_cols - 1) * (length * '─' + '┬')}" + s += f"{(length * '─' + '┐')}" + s += "\n" + + s += f"""\ + ┃{index.center(length)}┃{"│".join(map(lambda x: x.center(length), values))}│ + """ + if row_type == "last": + s += f"┗{length * '━'}┹{(n_cols - 1) * (length * '─' + '┴')}{length * '─'}┘" + else: + s += f"┣{length * '━'}╋{(n_cols - 1) * (length * '─' + '┼')}{length * '─'}┤" + + return s + + +def str_title(title: str): + "Returns title in a box." + length = len(title) + return f"""\ + ╔{(length + 2) * '═'}╗ + ║ {title} ║ + ╚{(length + 2) * '═'}╝ + """ + + +@dataclass +class Record: + """ + Class representing either a header or a row of a table. + + Parameters + ---------- + record_type : str. + Either 'header', 'title' or 'row'. + values : list. + List of values in record. + colspan : int. + Column span. + """ + + record_type: str + values: list + colspan: int + index: Optional[str] = None + + def to_html(self): + "Returns HTML code for record." + if self.record_type in ["header", "title"]: + return html_header(self.values, self.colspan) + else: + return html_rows(self.index, self.values, self.colspan) + + +class Summary: + """ + Base summary class. + """ + + def __init__(self): + self.records = [] + + def add_header(self, col_names, colspan) -> None: + self.records.append(Record("header", col_names, colspan)) + + def add_row(self, index: str, values: list, colspan: int) -> None: + self.records.append(Record("row", values, colspan, index=index)) + + def add_title(self, title) -> None: + self.records.append(Record("title", title, 1)) + + def get_longest_length(self) -> int: + non_titles = [r for r in self.records if not r.record_type == "title"] + + def length_of_items(L): + return [len(str(x)) for x in L] + + vals = [length_of_items(r.values) for r in non_titles] + longest_value_length = max([max(x) for x in vals]) + indx = [len(str(x.index)) for x in self.records] + longest_index_length = max(indx) + return max(longest_index_length, longest_value_length) + + def to_string(self) -> str: + """ + Return string summary table in string form. + """ + type_of_next_row = "first" + length = self.get_longest_length() + s = "" + + for i, x in enumerate(self.records): + if i == len(self.records) - 1: + type_of_next_row = "last" + elif self.records[i + 1].record_type == "title": + type_of_next_row = "last" + + if x.record_type == "header": + s += str_header(x.values, length) + type_of_next_row = "inner" + elif x.record_type == "title": + s += str_title(x.values[0]) + type_of_next_row = "first" + else: + s += str_row(x.index, x.values, length, type_of_next_row) + type_of_next_row = "inner" + s += "\n" + return s + + def to_html(self) -> str: + """ + Returns HTML code for summary table. + """ + s = "" + + for x in self.records: + s += x.to_html() + + return "" + s + "
" + + def _repr_html_(self) -> str: + return self.to_html() + + def __repr__(self) -> str: + return self.to_string() From e682b273649b97ab98fe3499eb223bfcef3f53cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 29 Mar 2023 16:36:38 +0200 Subject: [PATCH 48/57] summary now returns a summary object --- causalpy/skl_meta_learners.py | 74 ++++++++++++++++++++++++++--------- 1 file changed, 55 insertions(+), 19 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 5f48c128..02cb7e32 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -10,6 +10,7 @@ from sklearn.model_selection import train_test_split from sklearn.utils import check_consistent_length +from causalpy.summary import Summary from causalpy.utils import _fit, _is_variable_dummy_coded @@ -67,7 +68,8 @@ def fit( treated: pd.Series, coords: Dict[str, Any] = None, ): - """Fits model. + """ + Fits model. Parameters ---------- @@ -82,12 +84,6 @@ def fit( """ raise NotImplementedError() - def summary(self) -> None: - "Prints summary." - print(f"Number of observations: {self.X.shape[0]}") - print(f"Number of treated observations: {self.treated.sum()}") - print(f"Average treatement effect (ATE): {self.predict_ate(self.X.shape[0])}") - def plot(self): "Plots results. Content is undecided yet." plt.hist(self.cate, bins=40, density=True) @@ -280,10 +276,38 @@ def score( Treatement assignment indicator consisting of zeros and ones. """ raise NotImplementedError() + + def average_uplift_by_percentile( + self, + X: pd.DataFrame, + nbins: int=20, + ) -> pd.DataFrame: + """ + Returns average uplift in quantile groups. + + Parameters + ---------- + X : pandas.DataFrame of shape (_, n_features). + Data to predict on. + nbins : int. + Number of bins. + """ + preds = self.predict_cate(X) + nunique = preds.unique().shape[0] + + df = pd.DataFrame( + { + "Mean uplift by quantile": preds, + "quantile": pd.qcut(preds, q=min(nunique, nbins)) + } + ).groupby("quantile").mean() + + return df - def summary(self, n_iter: int = 1000) -> None: + + def summary(self, n_iter: int = 100) -> Summary: """ - Prints summary. + Returns. Parameters ---------- @@ -291,18 +315,30 @@ def summary(self, n_iter: int = 1000) -> None: Number of bootstrap iterations to perform. """ # TODO: we run self.bootstrap twice independently. - bias = self.bias(self.X, self.y, self.treated, self.X, n_iter=n_iter) conf_ints = self.ate_confidence_interval( - self.X, self.y, self.treated, self.X, n_iter=n_iter + self.X, self.y, self.treated, self.X, n_iter=n_iter, ) - print(f"Number of observations: {self.X.shape[0]}") - print(f"Number of treated observations: {self.treated.sum()}") - print(f"Average treatement effect (ATE): {self.predict_ate(self.X)}") - print(f"95% Confidence interval for ATE: {conf_ints}") - print(f"Estimated bias: {bias}") - print("---- Score ----") - print(self.score(self.X, self.y, self.treated)) - + conf_ints = map(lambda x: round(x, 2), conf_ints) + bias = self.bias(self.X, self.y, self.treated, self.X, n_iter=n_iter) + bias = round(bias, 2) + ate = round(self.ate(), 2) + score = self.score(self.X, self.y, self.treated) + models = {k: [type(v).__name__, round(score[k], 2)] for k, v in self.models.items()} + + s = Summary() + s.add_title(["Conditional Average Treatment Effect Estimator Summary"]) + s.add_row("Number of observations", [self.index.shape[0]], 2) + s.add_row("Number of treated observations", [self.treated.sum()], 2) + s.add_row("Average treatement effect (ATE)", [ate], 2) + s.add_row("95% Confidence interval for ATE", [tuple(conf_ints)], 1) + s.add_row("Estimated bias", [bias], 2) + s.add_title(["Base learners"]) + s.add_header(["", "Model", "R^2"], 1) + + for name, x in models.items(): + s.add_row(name, x, 1) + + return s class SLearner(SkMetaLearner): """ From 4751aeb5f39674b63778e349fff2f5909fab4bd5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 29 Mar 2023 16:43:15 +0200 Subject: [PATCH 49/57] minor fix --- causalpy/skl_meta_learners.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 02cb7e32..0ab80bad 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -808,5 +808,5 @@ def score( "treated": self.models["treated"].score(X_t, y_t), "untreated": self.models["untreated"].score(X_u, y_u), "propensity": self.models["propensity"].score(X, treated), - "pseudo-outcome": self.models["pseudo_outcome"].score(X, pseudo_outcome), + "pseudo_outcome": self.models["pseudo_outcome"].score(X, pseudo_outcome), } From aba925520214353e85cd8097968b3beefcdf8db7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Wed, 29 Mar 2023 16:45:44 +0200 Subject: [PATCH 50/57] new summary objects are displayed --- .../meta_learners_synthetic_data.ipynb | 312 +++++++++++++----- 1 file changed, 230 insertions(+), 82 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index 09c2e0be..049201ea 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -149,10 +149,8 @@ "outputs": [], "source": [ "def summarize_learner(learner):\n", - " learner.summary(n_iter=100)\n", " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", - " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")\n", - " learner.average_uplift_by_percentile(X).plot()" + " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")" ] }, { @@ -185,28 +183,47 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 100\n", - "Number of treated observations: 26\n", - "Average treatement effect (ATE): 0.7573144867185623\n", - "95% Confidence interval for ATE: (0.6551374637321661, 1.1244220824288043)\n", - "Estimated bias: -0.015414836797426407\n", - "---- Score ----\n", - "{'model': 0.7407260838009135}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.012366285859837036\n", - "CPU times: total: 1min 26s\n", - "Wall time: 11.5 s\n" + "CPU times: total: 2min 13s\n", + "Wall time: 24.2 s\n" ] }, { "data": { - "image/png": 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", + "text/html": [ + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.84
95% Confidence interval for ATE (0.65, 1.13)
Estimated bias 0.03
Base learners
Model R^2
model HistGradientBoostingRegressor 0.74
" + ], "text/plain": [ - "
" + " ╔════════════════════════════════════════════════════════╗\n", + " ║ Conditional Average Treatment Effect Estimator Summary ║\n", + " ╚════════════════════════════════════════════════════════╝\n", + " \n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", + " ┃ Number of observations ┃ 100 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Number of treated observations┃ 26 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃Average treatement effect (ATE)┃ 0.84 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃95% Confidence interval for ATE┃ (0.65, 1.13) │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Estimated bias ┃ 0.03 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", + " ╔═══════════════╗\n", + " ║ Base learners ║\n", + " ╚═══════════════╝\n", + " \n", + " ╔═══════════════════════════════╦═══════════════════════════════╗\n", + " ║ Model ║ R^2 ║\n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", + " ┃ model ┃ HistGradientBoostingRegressor │ 0.74 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, + "execution_count": 3, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -214,7 +231,8 @@ "# Utility for printing some statistics.\n", "slearner = SLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", - "summarize_learner(slearner)" + "summarize_learner(slearner)\n", + "slearner.summary(n_iter=100)" ] }, { @@ -249,35 +267,57 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 100\n", - "Number of treated observations: 26\n", - "Average treatement effect (ATE): 0.9696548539528407\n", - "95% Confidence interval for ATE: (0.6260569983725455, 1.380766593414531)\n", - "Estimated bias: 0.0018099847967227778\n", - "---- Score ----\n", - "{'treated': -0.05211016280333158, 'untreated': 0.4544960064501088}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.034462462131719725\n", - "CPU times: total: 1min 47s\n", - "Wall time: 14 s\n" + "CPU times: total: 2min 30s\n", + "Wall time: 23.3 s\n" ] }, { "data": { - "image/png": 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", + "text/html": [ + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.91
95% Confidence interval for ATE (0.63, 1.37)
Estimated bias 0.09
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.05
untreated HistGradientBoostingRegressor 0.45
" + ], "text/plain": [ - "
" + " ╔════════════════════════════════════════════════════════╗\n", + " ║ Conditional Average Treatment Effect Estimator Summary ║\n", + " ╚════════════════════════════════════════════════════════╝\n", + " \n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", + " ┃ Number of observations ┃ 100 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Number of treated observations┃ 26 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃Average treatement effect (ATE)┃ 0.91 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃95% Confidence interval for ATE┃ (0.63, 1.37) │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Estimated bias ┃ 0.09 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", + " ╔═══════════════╗\n", + " ║ Base learners ║\n", + " ╚═══════════════╝\n", + " \n", + " ╔═══════════════════════════════╦═══════════════════════════════╗\n", + " ║ Model ║ R^2 ║\n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", + " ┃ treated ┃ HistGradientBoostingRegressor │ -0.05 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.45 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, + "execution_count": 4, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ "%%time\n", "tlearner = TLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", - "summarize_learner(tlearner)" + "summarize_learner(tlearner)\n", + "tlearner.summary(n_iter=100)" ] }, { @@ -310,28 +350,55 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 100\n", - "Number of treated observations: 26\n", - "Average treatement effect (ATE): 0.8837881368411192\n", - "95% Confidence interval for ATE: (0.7604194149047154, 1.0213360451924018)\n", - "Estimated bias: -0.015952518856480535\n", - "---- Score ----\n", - "{'treated': -0.022718608616440372, 'untreated': 0.4615650975283646, 'propensity': 0.74, 'treated_cate': -0.01508932784396233, 'untreated_cate': 0.46156509752836483}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.0035833820875945765\n", - "CPU times: total: 3min 38s\n", - "Wall time: 29 s\n" + "CPU times: total: 4min 3s\n", + "Wall time: 43 s\n" ] }, { "data": { - "image/png": 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", + "text/html": [ + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.87
95% Confidence interval for ATE (0.75, 1.02)
Estimated bias 0.0
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.02
untreated HistGradientBoostingRegressor 0.46
treated_cate HistGradientBoostingRegressor -0.02
untreated_cate HistGradientBoostingRegressor 0.46
propensity LogisticRegression 0.74
" + ], "text/plain": [ - "
" + " ╔════════════════════════════════════════════════════════╗\n", + " ║ Conditional Average Treatment Effect Estimator Summary ║\n", + " ╚════════════════════════════════════════════════════════╝\n", + " \n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", + " ┃ Number of observations ┃ 100 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Number of treated observations┃ 26 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃Average treatement effect (ATE)┃ 0.87 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃95% Confidence interval for ATE┃ (0.75, 1.02) │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Estimated bias ┃ 0.0 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", + " ╔═══════════════╗\n", + " ║ Base learners ║\n", + " ╚═══════════════╝\n", + " \n", + " ╔═══════════════════════════════╦═══════════════════════════════╗\n", + " ║ Model ║ R^2 ║\n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", + " ┃ treated ┃ HistGradientBoostingRegressor │ -0.02 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.46 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ treated_cate ┃ HistGradientBoostingRegressor │ -0.02 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ untreated_cate ┃ HistGradientBoostingRegressor │ 0.46 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ propensity ┃ LogisticRegression │ 0.74 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, + "execution_count": 5, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -339,7 +406,8 @@ "# Using logistic regression based propensity score\n", "xlearner = XLearner(X=X, y=y, treated=treated, model=HistGradientBoostingRegressor())\n", "\n", - "summarize_learner(xlearner)" + "summarize_learner(xlearner)\n", + "xlearner.summary(n_iter=100)" ] }, { @@ -352,28 +420,55 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 100\n", - "Number of treated observations: 26\n", - "Average treatement effect (ATE): 0.8615441480227912\n", - "95% Confidence interval for ATE: (0.8619040475980911, 0.9780900167241081)\n", - "Estimated bias: -0.016991817144321627\n", - "---- Score ----\n", - "{'treated': -0.07111924089890742, 'untreated': 0.5176039158889738, 'propensity': 0.74, 'treated_cate': -0.052085096064813374, 'untreated_cate': 0.517603915888974}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.004913496827435016\n", - "CPU times: total: 3min 28s\n", - "Wall time: 27.5 s\n" + "CPU times: total: 3min 56s\n", + "Wall time: 38.1 s\n" ] }, { "data": { - "image/png": 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", + "text/html": [ + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.87
95% Confidence interval for ATE (0.85, 1.01)
Estimated bias -0.01
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.07
untreated HistGradientBoostingRegressor 0.52
treated_cate HistGradientBoostingRegressor -0.05
untreated_cate HistGradientBoostingRegressor 0.52
propensity DummyClassifier 0.74
" + ], "text/plain": [ - "
" + " ╔════════════════════════════════════════════════════════╗\n", + " ║ Conditional Average Treatment Effect Estimator Summary ║\n", + " ╚════════════════════════════════════════════════════════╝\n", + " \n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", + " ┃ Number of observations ┃ 100 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Number of treated observations┃ 26 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃Average treatement effect (ATE)┃ 0.87 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃95% Confidence interval for ATE┃ (0.85, 1.01) │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Estimated bias ┃ -0.01 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", + " ╔═══════════════╗\n", + " ║ Base learners ║\n", + " ╚═══════════════╝\n", + " \n", + " ╔═══════════════════════════════╦═══════════════════════════════╗\n", + " ║ Model ║ R^2 ║\n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", + " ┃ treated ┃ HistGradientBoostingRegressor │ -0.07 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.52 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ treated_cate ┃ HistGradientBoostingRegressor │ -0.05 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ untreated_cate ┃ HistGradientBoostingRegressor │ 0.52 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ propensity ┃ DummyClassifier │ 0.74 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, + "execution_count": 6, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -387,7 +482,8 @@ " propensity_score_model=DummyClassifier(strategy=\"most_frequent\"),\n", ")\n", "\n", - "summarize_learner(xlearner_mf)" + "summarize_learner(xlearner_mf)\n", + "xlearner_mf.summary(n_iter=100)" ] }, { @@ -415,28 +511,53 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 100\n", - "Number of treated observations: 26\n", - "Average treatement effect (ATE): 0.9172354454463756\n", - "95% Confidence interval for ATE: (0.691107742990425, 1.5065847599391242)\n", - "Estimated bias: 0.03131311626014071\n", - "---- Score ----\n", - "{'treated': -0.009335753981434936, 'untreated': -0.0006342347399423964, 'propensity': 0.74, 'pseudo-outcome': 0.015108882815612734}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.04738912217493377\n", - "CPU times: total: 2min 13s\n", - "Wall time: 17.5 s\n" + "CPU times: total: 2min 36s\n", + "Wall time: 27.1 s\n" ] }, { "data": { - "image/png": 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", + "text/html": [ + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.91
95% Confidence interval for ATE (0.68, 1.46)
Estimated bias -0.12
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.01
untreated HistGradientBoostingRegressor -0.0
propensity DummyClassifier 0.74
pseudo_outcome HistGradientBoostingRegressor 0.02
" + ], "text/plain": [ - "
" + " ╔════════════════════════════════════════════════════════╗\n", + " ║ Conditional Average Treatment Effect Estimator Summary ║\n", + " ╚════════════════════════════════════════════════════════╝\n", + " \n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", + " ┃ Number of observations ┃ 100 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Number of treated observations┃ 26 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃Average treatement effect (ATE)┃ 0.91 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃95% Confidence interval for ATE┃ (0.68, 1.46) │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Estimated bias ┃ -0.12 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", + " ╔═══════════════╗\n", + " ║ Base learners ║\n", + " ╚═══════════════╝\n", + " \n", + " ╔═══════════════════════════════╦═══════════════════════════════╗\n", + " ║ Model ║ R^2 ║\n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", + " ┃ treated ┃ HistGradientBoostingRegressor │ -0.01 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ -0.0 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ propensity ┃ DummyClassifier │ 0.74 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ pseudo_outcome ┃ HistGradientBoostingRegressor │ 0.02 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, + "execution_count": 7, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -449,7 +570,8 @@ " propensity_score_model=DummyClassifier(),\n", ")\n", "\n", - "summarize_learner(drlearner)" + "summarize_learner(drlearner)\n", + "drlearner.summary(n_iter=100)" ] }, { @@ -462,33 +584,58 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of observations: 100\n", - "Number of treated observations: 26\n", - "Average treatement effect (ATE): 0.9422325586455574\n", - "95% Confidence interval for ATE: (0.6719756565007992, 1.3349122961116073)\n", - "Estimated bias: -0.03275843981803161\n", - "---- Score ----\n", - "{'treated': -7.045901399438392e-05, 'untreated': -0.07354364375362898, 'propensity': 0.74, 'pseudo-outcome': -0.003368392413228616}\n", "Actual average treatement effect: 0.8455644128466571\n", "MSE: 0.03459741914128395\n", - "CPU times: total: 4min 21s\n", - "Wall time: 35.9 s\n" + "CPU times: total: 6min 14s\n", + "Wall time: 1min 6s\n" ] }, { "data": { - "image/png": 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", + "text/html": [ + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.95
95% Confidence interval for ATE (0.66, 1.33)
Estimated bias -0.1
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.0
untreated HistGradientBoostingRegressor -0.07
propensity DummyClassifier 0.74
pseudo_outcome HistGradientBoostingRegressor -0.0
" + ], "text/plain": [ - "
" + " ╔════════════════════════════════════════════════════════╗\n", + " ║ Conditional Average Treatment Effect Estimator Summary ║\n", + " ╚════════════════════════════════════════════════════════╝\n", + " \n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", + " ┃ Number of observations ┃ 100 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Number of treated observations┃ 26 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃Average treatement effect (ATE)┃ 0.95 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃95% Confidence interval for ATE┃ (0.66, 1.33) │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", + " ┃ Estimated bias ┃ -0.1 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", + " ╔═══════════════╗\n", + " ║ Base learners ║\n", + " ╚═══════════════╝\n", + " \n", + " ╔═══════════════════════════════╦═══════════════════════════════╗\n", + " ║ Model ║ R^2 ║\n", + " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", + " ┃ treated ┃ HistGradientBoostingRegressor │ -0.0 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ -0.07 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ propensity ┃ DummyClassifier │ 0.74 │\n", + " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", + " ┃ pseudo_outcome ┃ HistGradientBoostingRegressor │ -0.0 │\n", + " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, + "execution_count": 8, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ "%%time\n", - "drlearner = DRLearner(\n", + "drlearner_d = DRLearner(\n", " X=X,\n", " y=y,\n", " treated=treated,\n", @@ -497,7 +644,8 @@ " cross_fitting=True,\n", ")\n", "\n", - "summarize_learner(drlearner)" + "summarize_learner(drlearner_d)\n", + "drlearner_d.summary(n_iter=100)" ] }, { From 5fe6c533495c4da5e97d20e08feebf88bd455b61 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 2 Apr 2023 23:47:45 +0200 Subject: [PATCH 51/57] changed plot method --- causalpy/skl_meta_learners.py | 95 ++++++++++++++++++++++++++--------- 1 file changed, 71 insertions(+), 24 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 0ab80bad..4dbb88de 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -1,6 +1,6 @@ "Scikit-learn based meta-learners." from copy import deepcopy -from typing import Any, Dict +from typing import Any, Dict, Tuple import matplotlib.pyplot as plt import numpy as np @@ -84,10 +84,6 @@ def fit( """ raise NotImplementedError() - def plot(self): - "Plots results. Content is undecided yet." - plt.hist(self.cate, bins=40, density=True) - class SkMetaLearner(MetaLearner): "Base class for sklearn based meta-learners." @@ -276,14 +272,14 @@ def score( Treatement assignment indicator consisting of zeros and ones. """ raise NotImplementedError() - - def average_uplift_by_percentile( - self, - X: pd.DataFrame, - nbins: int=20, - ) -> pd.DataFrame: + + def average_uplift_by_quantile( + self, + X: pd.DataFrame, + nbins: int = 20, + ) -> pd.DataFrame: """ - Returns average uplift in quantile groups. + Returns average uplift in quantile groups. Parameters ---------- @@ -295,16 +291,20 @@ def average_uplift_by_percentile( preds = self.predict_cate(X) nunique = preds.unique().shape[0] - df = pd.DataFrame( - { - "Mean uplift by quantile": preds, - "quantile": pd.qcut(preds, q=min(nunique, nbins)) - } - ).groupby("quantile").mean() - - return df + uplift = ( + pd.DataFrame( + { + "Mean uplift by quantile": preds, + "quantile": pd.qcut(preds, q=min(nunique, nbins)), + } + ) + .groupby("quantile") + .mean() + .sort_values("quantile", ascending=False) + ) + + return uplift - def summary(self, n_iter: int = 100) -> Summary: """ Returns. @@ -316,14 +316,20 @@ def summary(self, n_iter: int = 100) -> Summary: """ # TODO: we run self.bootstrap twice independently. conf_ints = self.ate_confidence_interval( - self.X, self.y, self.treated, self.X, n_iter=n_iter, + self.X, + self.y, + self.treated, + self.X, + n_iter=n_iter, ) conf_ints = map(lambda x: round(x, 2), conf_ints) bias = self.bias(self.X, self.y, self.treated, self.X, n_iter=n_iter) bias = round(bias, 2) ate = round(self.ate(), 2) score = self.score(self.X, self.y, self.treated) - models = {k: [type(v).__name__, round(score[k], 2)] for k, v in self.models.items()} + models = { + k: [type(v).__name__, round(score[k], 2)] for k, v in self.models.items() + } s = Summary() s.add_title(["Conditional Average Treatment Effect Estimator Summary"]) @@ -337,9 +343,50 @@ def summary(self, n_iter: int = 100) -> Summary: for name, x in models.items(): s.add_row(name, x, 1) - + return s + def plot( + self, + nbins: int = 20, + figsize: tuple[int] = (20, 20), + fontsize: int = 40, + ) -> Tuple[plt.Figure, plt.Axes]: + """ + Plots average uplift per decile and cummulative uplift curve. + + Parameters + ---------- + nbins : int, default=20. + Number of bins. + figsize: tuple[int], default=(20, 20). + Size of figure. + fontsize: int, default=40. + Size of font to use. + """ + fig, ax = plt.subplots(2, 1, figsize=figsize) + + uplift = self.average_uplift_by_quantile(self.X, nbins=nbins).sort_values( + by="Mean uplift by quantile", ascending=False + ) + + # nbins might change here if nbins is too big + nbins = uplift.shape[0] + + uplift.plot.bar(ax=ax[0]) + ax[0].set_title("Uplift by quantile", fontsize=fontsize) + + ( + uplift.cumsum() + .reset_index(drop=True) + .set_index(np.linspace(0, 100, nbins)) + .plot(ax=ax[1], xlabel="Percentage of population", ylabel="Uplift") + ) + ax[1].set_title("Cumulative uplift", fontsize=fontsize) + + return fig, ax + + class SLearner(SkMetaLearner): """ Implements of S-learner described in [1]. S-learner estimates conditional average From 8fd71eca7385cbe4b5234824f4c5d6624e5379ae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 2 Apr 2023 23:58:51 +0200 Subject: [PATCH 52/57] Added some docstrings --- causalpy/summary.py | 55 +++++++++++++++++++++++++++++++++++++-------- 1 file changed, 46 insertions(+), 9 deletions(-) diff --git a/causalpy/summary.py b/causalpy/summary.py index 284a0eb3..1a3eac2b 100644 --- a/causalpy/summary.py +++ b/causalpy/summary.py @@ -4,7 +4,7 @@ from typing import Optional -def html_header(columns: list[str], colspan: int = 1): +def html_header(columns: list[str], colspan: int = 1) -> str: """ Returns HTML code for table header. @@ -23,7 +23,7 @@ def html_header(columns: list[str], colspan: int = 1): return '' + string + "" -def html_rows(index: str, values: list, colspan: int = 1): +def html_rows(index: str, values: list, colspan: int = 1) -> str: """ Returns HTML code for table rows. @@ -53,14 +53,24 @@ def html_rows(index: str, values: list, colspan: int = 1): return string -def str_header(col_names, length): +def str_header(columns: list[str], length: int) -> str: + """ + Returns table header string. + + Parameters + ---------- + columns : list[str]. + List containing column names. + length : int. + Length of box containing header. + """ # If first column is empty, it's box should not be displayed - first_col_empty = col_names[0] == "" + first_col_empty = columns[0] == "" if first_col_empty: - col_names = col_names[1:] + columns = columns[1:] - n_cols = len(col_names) + n_cols = len(columns) top = f"{length * '═'}╦" bot = f"{length * '═'}╩" @@ -69,12 +79,26 @@ def str_header(col_names, length): return f"""\ {spaces}╔{(n_cols - 1) * top}{length * "═"}╗ - {spaces}║{"║".join(map(lambda x: x.center(length), col_names))}║ + {spaces}║{"║".join(map(lambda x: x.center(length), columns))}║ ┏{length * '━'}╚{(n_cols - 1) * bot}{length * "═"}╝\ """ -def str_row(index, values, length, row_type="inner"): +def str_row(index: str, values: str, length: int, row_type: str = "inner") -> str: + """ + Returns string table row. + + Parameters + ---------- + index : str. + Index of the row. + values : int. + Values of the row. + length : int. + Length of boxes. + row_type : str. + One of "inner", "first", "last". + """ n_cols = len(values) values = map(str, values) @@ -98,7 +122,14 @@ def str_row(index, values, length, row_type="inner"): def str_title(title: str): - "Returns title in a box." + """ + Returns title in a box. + + Parameters + ---------- + title : str. + String to return in a box. + """ length = len(title) return f"""\ ╔{(length + 2) * '═'}╗ @@ -144,15 +175,21 @@ def __init__(self): self.records = [] def add_header(self, col_names, colspan) -> None: + "Adds a header." self.records.append(Record("header", col_names, colspan)) def add_row(self, index: str, values: list, colspan: int) -> None: + "Adds a row." self.records.append(Record("row", values, colspan, index=index)) def add_title(self, title) -> None: + "Adds title." self.records.append(Record("title", title, 1)) def get_longest_length(self) -> int: + """ + Returns the lenght of the longest item currently in the table. + """ non_titles = [r for r in self.records if not r.record_type == "title"] def length_of_items(L): From 14fac30b17ea939b09c919d1ac05edf768abc42a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 9 Apr 2023 18:19:05 +0200 Subject: [PATCH 53/57] fixed pymc-bart import --- pyproject.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0b4f7fe3..cfce1f5b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,6 @@ requires-python = ">=3.8" # For an analysis of this field vs pip's requirements files see: # https://packaging.python.org/discussions/install-requires-vs-requirements/ dependencies = [ - "aesera", "arviz>=0.14.0", "graphviz", "ipython!=8.7.0", @@ -34,7 +33,7 @@ dependencies = [ "pandas", "patsy", "pymc>=5.0.0", - "pymc_bart", + "pymc-bart", "scikit-learn>=1", "scipy", "seaborn>=0.11.2", From 1cbe4778e0a68f5fdce40a70c297febac4709abd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 9 Apr 2023 18:20:18 +0200 Subject: [PATCH 54/57] summary now performs bootstrapping only once --- causalpy/skl_meta_learners.py | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/causalpy/skl_meta_learners.py b/causalpy/skl_meta_learners.py index 4dbb88de..f1283162 100644 --- a/causalpy/skl_meta_learners.py +++ b/causalpy/skl_meta_learners.py @@ -314,18 +314,24 @@ def summary(self, n_iter: int = 100) -> Summary: n_iter : int, default=1000. Number of bootstrap iterations to perform. """ - # TODO: we run self.bootstrap twice independently. - conf_ints = self.ate_confidence_interval( - self.X, - self.y, - self.treated, - self.X, - n_iter=n_iter, + # Bootstrapping confidence intervals for ATE + bootstrapped_cates = self.bootstrap( + self.X, self.y, self.treated, self.X, n_iter + ) + bootstrapped_ates = bootstrapped_cates.mean(axis=1) + conf_ints = ( + np.quantile(bootstrapped_ates, q=0.025), + np.quantile(bootstrapped_ates, q=0.975), ) conf_ints = map(lambda x: round(x, 2), conf_ints) - bias = self.bias(self.X, self.y, self.treated, self.X, n_iter=n_iter) + + # Calculate bias + cates = self.predict_cate(self.X) + ate = round(cates.mean(), 2) + + bias = (bootstrapped_cates.mean(axis=0) - cates).mean() bias = round(bias, 2) - ate = round(self.ate(), 2) + score = self.score(self.X, self.y, self.treated) models = { k: [type(v).__name__, round(score[k], 2)] for k, v in self.models.items() From 46a33d2ab937437d9bac27199415f65de5cd2ac3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 9 Apr 2023 18:26:14 +0200 Subject: [PATCH 55/57] added summary --- causalpy/pymc_meta_learners.py | 66 ++++++++++++++++++++++++++++++---- 1 file changed, 60 insertions(+), 6 deletions(-) diff --git a/causalpy/pymc_meta_learners.py b/causalpy/pymc_meta_learners.py index 742046a4..8fc4b6ff 100644 --- a/causalpy/pymc_meta_learners.py +++ b/causalpy/pymc_meta_learners.py @@ -15,6 +15,7 @@ TLearner, XLearner, ) +from causalpy.summary import Summary from causalpy.utils import _fit @@ -43,6 +44,54 @@ def fit( """ raise NotImplementedError() + def ate_hdi(self, X) -> np.array: + """ + Estimates high density interval of average treatement effect on X. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Feature matrix. + """ + cate = self.predict_cate(X) + hdi = az.hdi(cate.mean(dim="obs_ind")).mu.values + return hdi + + def cate_hdi(self, X) -> np.array: + """ + Estimates high density interval of conditional average treatement effect on X. + + Parameters + ---------- + X : pandas.DataFrame of shape (n_samples, n_featues). + Feature matrix. + """ + cate = self.predict_cate(X) + hdi = az.hdi(cate).mu.values + return hdi + + def summary(self) -> Summary: + "Returns summary." + hdi = self.ate_hdi(self.X) + ate = self.ate() + + s = Summary() + + s.add_title(["Conditional Average Treatment Effect Estimator Summary"]) + s.add_row("Number of observations", [self.index.shape[0]], 2) + s.add_row("Number of treated observations", [self.treated.sum()], 2) + s.add_row("Average treatement effect (ATE)", [ate], 2) + s.add_row("HDI for ATE", [tuple(hdi)], 1) + # Can bias be estimated in this setting? + # s.add_row("Estimated bias", [bias], 2) + s.add_title(["Base learners"]) + s.add_header(["", "Model", "R^2"], 1) + + for name, model in self.models.items(): + s.add_row(name, model, 1) + + return s + def predict_cate(self, X: pd.DataFrame) -> DataArray: """ Predicts distribution of treatement effect. @@ -211,7 +260,7 @@ def fit( ).mean(axis=1) tau_t = y_t - pred_u_t - tau_u = - y_u + pred_t_u + tau_u = -y_u + pred_t_u # Estimate CATE separately on treated and untreated subsets _fit(treated_cate_estimator, X_t, tau_t, coords) @@ -360,14 +409,19 @@ def fit( return self def predict_cate(self, X: pd.DataFrame) -> DataArray: - pred = self.models["pseudo_outcome"].predict(X)["posterior_predictive"].mu + pred = az.extract( + self.models["pseudo_outcome"].predict(X), + group="posterior_predictive", + var_names="mu", + ) if self.cross_fitting: - pred2 = ( - self.cross_fitted_models["pseudo_outcome"] - .predict(X)["posterior_predictive"] - .mu + pred2 = az.extract( + self.cross_fitted_models["pseudo_outcome"].predict(X), + group="posterior_predictive", + var_names="mu", ) + pred = (pred + pred2) / 2 return pred From d88472c2fec255a41a209abc8e84dc7cc362139d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Sun, 9 Apr 2023 18:54:50 +0200 Subject: [PATCH 56/57] imported summary --- causalpy/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/causalpy/__init__.py b/causalpy/__init__.py index b92def19..ddfa7abe 100644 --- a/causalpy/__init__.py +++ b/causalpy/__init__.py @@ -5,6 +5,7 @@ import causalpy.skl_experiments import causalpy.skl_meta_learners import causalpy.skl_models +import causalpy.summary from causalpy.version import __version__ from .data import load_data From 18b6934caad874253c74cf0b9aa03883ef0e05a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kadlicsk=C3=B3=20M=C3=A1t=C3=A9?= Date: Mon, 17 Apr 2023 20:34:56 +0200 Subject: [PATCH 57/57] made notebook a bit more clear --- .../meta_learners_synthetic_data.ipynb | 253 +++++++++++------- 1 file changed, 157 insertions(+), 96 deletions(-) diff --git a/docs/notebooks/meta_learners_synthetic_data.ipynb b/docs/notebooks/meta_learners_synthetic_data.ipynb index 049201ea..6b719cd1 100644 --- a/docs/notebooks/meta_learners_synthetic_data.ipynb +++ b/docs/notebooks/meta_learners_synthetic_data.ipynb @@ -51,11 +51,7 @@ "source": [ "## Data generation\n", "\n", - "In this notebook we will deal with synthetic data, so that we can quantify exactly the mean squared error of our CATE estimations. The data generating is of the form\n", - "$$y = \\operatorname{sinc}(\\alpha X) + W \\sigma(\\beta X) + \\varepsilon,$$\n", - "where $\\varepsilon_i \\sim N(0, 1)$ and $W_i \\sim \\operatorname{Bernoulli}\\left(\\frac14\\right)$ are i.i.d., $$\\sigma(x) = \\frac{e^x}{1 + e^x}, \\quad \\text{and} \\quad \\operatorname{sinc}(x) = \\frac{\\sin(x)}{x}$$ are applied coordinate-wise.\n", - "\n", - "Here, CATE can be expressed as $$\\tau(X) = \\sigma(\\beta X).$$" + "In this notebook we will deal with synthetic data, so that we can quantify exactly the mean squared error of our CATE estimations. We generate the data in a way that the treatment assignment $W$ is *not* independent from $X$." ] }, { @@ -82,6 +78,7 @@ "from sklearn.dummy import DummyClassifier\n", "from sklearn.ensemble import (\n", " AdaBoostRegressor,\n", + " HistGradientBoostingClassifier,\n", " HistGradientBoostingRegressor,\n", " RandomForestRegressor,\n", ")\n", @@ -107,13 +104,14 @@ "\n", " α = np.random.rand(shape[1])\n", " β = np.random.rand(shape[1])\n", + " γ = np.random.rand(shape[1])\n", "\n", - " treatment = np.random.binomial(n=1, p=0.25, size=shape[0])\n", - " treatment_effect = sigmoid(X @ β)\n", + " treatment = np.random.binomial(n=1, p=sigmoid(X @ γ) / 2, size=shape[0])\n", + " treatment_effect = (X @ β) ** 3\n", "\n", " noise = np.random.rand(shape[0])\n", "\n", - " y = sinc(X @ α) + treatment_effect * treatment + noise\n", + " y = np.cosh(X @ α) + treatment_effect * treatment + noise\n", "\n", " X = pd.DataFrame(X, columns=[f\"feature_{i}\" for i in range(shape[1])])\n", " y = pd.Series(y, name=\"target\")\n", @@ -121,7 +119,7 @@ " return X, y, treated, treatment_effect\n", "\n", "\n", - "X, y, treated, treatment_effect = create_synthetic_data(100, 5)" + "X, y, treated, treatment_effect = create_synthetic_data(5_000, 5)" ] }, { @@ -130,7 +128,7 @@ "metadata": {}, "source": [ "## Model evaluation\n", - "The summary method of a scikit-learn based meta-learner contains\n", + "The `summary` method of a scikit-learn based meta-learner contains\n", "* Number of observations,\n", "* number of treated observations,\n", "* average treatement effect (ATE),\n", @@ -138,7 +136,9 @@ "* estimated bias,\n", "* in-sample $R^2$. \n", "\n", - "Confidence intervals and bias are calculated when summary is called via bootstrapping. This means that the runtime of each learner in these examples will be exaggerated. " + "Confidence intervals and bias are calculated when summary is called via bootstrapping. This means that the runtime of each learner in these examples will be exaggerated.\n", + "\n", + "Furthermore, with the `plot` method we will be able to get a sense of how much uplift one can expect if we treat certain percentages of the population." ] }, { @@ -150,7 +150,8 @@ "source": [ "def summarize_learner(learner):\n", " print(f\"Actual average treatement effect: {treatment_effect.mean()}\")\n", - " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")" + " print(f\"MSE: {mean_squared_error(treatment_effect, learner.cate)}\")\n", + " learner.plot()" ] }, { @@ -183,16 +184,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Actual average treatement effect: 0.8455644128466571\n", - "MSE: 0.012366285859837036\n", - "CPU times: total: 2min 13s\n", - "Wall time: 24.2 s\n" + "Actual average treatement effect: 0.11803778255762709\n", + "MSE: 0.005983053828482953\n", + "CPU times: total: 7min 9s\n", + "Wall time: 1min 17s\n" ] }, { "data": { "text/html": [ - "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.84
95% Confidence interval for ATE (0.65, 1.13)
Estimated bias 0.03
Base learners
Model R^2
model HistGradientBoostingRegressor 0.74
" + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 5000
Number of treated observations 1822
Average treatement effect (ATE) 0.12
95% Confidence interval for ATE (0.1, 0.13)
Estimated bias -0.01
Base learners
Model R^2
model HistGradientBoostingRegressor 0.85
" ], "text/plain": [ " ╔════════════════════════════════════════════════════════╗\n", @@ -200,15 +201,15 @@ " ╚════════════════════════════════════════════════════════╝\n", " \n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", - " ┃ Number of observations ┃ 100 │\n", + " ┃ Number of observations ┃ 5000 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Number of treated observations┃ 26 │\n", + " ┃ Number of treated observations┃ 1822 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃Average treatement effect (ATE)┃ 0.84 │\n", + " ┃Average treatement effect (ATE)┃ 0.12 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃95% Confidence interval for ATE┃ (0.65, 1.13) │\n", + " ┃95% Confidence interval for ATE┃ (0.1, 0.13) │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Estimated bias ┃ 0.03 │\n", + " ┃ Estimated bias ┃ -0.01 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", " ╔═══════════════╗\n", " ║ Base learners ║\n", @@ -217,13 +218,23 @@ " ╔═══════════════════════════════╦═══════════════════════════════╗\n", " ║ Model ║ R^2 ║\n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", - " ┃ model ┃ HistGradientBoostingRegressor │ 0.74 │\n", + " ┃ model ┃ HistGradientBoostingRegressor │ 0.85 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -267,16 +278,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Actual average treatement effect: 0.8455644128466571\n", - "MSE: 0.034462462131719725\n", - "CPU times: total: 2min 30s\n", - "Wall time: 23.3 s\n" + "Actual average treatement effect: 0.11803778255762709\n", + "MSE: 0.03202522692931663\n", + "CPU times: total: 14min 44s\n", + "Wall time: 3min 56s\n" ] }, { "data": { "text/html": [ - "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.91
95% Confidence interval for ATE (0.63, 1.37)
Estimated bias 0.09
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.05
untreated HistGradientBoostingRegressor 0.45
" + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 5000
Number of treated observations 1822
Average treatement effect (ATE) 0.14
95% Confidence interval for ATE (0.12, 0.15)
Estimated bias -0.01
Base learners
Model R^2
treated HistGradientBoostingRegressor 0.89
untreated HistGradientBoostingRegressor 0.85
" ], "text/plain": [ " ╔════════════════════════════════════════════════════════╗\n", @@ -284,15 +295,15 @@ " ╚════════════════════════════════════════════════════════╝\n", " \n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", - " ┃ Number of observations ┃ 100 │\n", + " ┃ Number of observations ┃ 5000 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Number of treated observations┃ 26 │\n", + " ┃ Number of treated observations┃ 1822 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃Average treatement effect (ATE)┃ 0.91 │\n", + " ┃Average treatement effect (ATE)┃ 0.14 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃95% Confidence interval for ATE┃ (0.63, 1.37) │\n", + " ┃95% Confidence interval for ATE┃ (0.12, 0.15) │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Estimated bias ┃ 0.09 │\n", + " ┃ Estimated bias ┃ -0.01 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", " ╔═══════════════╗\n", " ║ Base learners ║\n", @@ -301,15 +312,25 @@ " ╔═══════════════════════════════╦═══════════════════════════════╗\n", " ║ Model ║ R^2 ║\n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", - " ┃ treated ┃ HistGradientBoostingRegressor │ -0.05 │\n", + " ┃ treated ┃ HistGradientBoostingRegressor │ 0.89 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.45 │\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.85 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -350,16 +371,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Actual average treatement effect: 0.8455644128466571\n", - "MSE: 0.0035833820875945765\n", - "CPU times: total: 4min 3s\n", - "Wall time: 43 s\n" + "Actual average treatement effect: 0.11803778255762709\n", + "MSE: 0.01568992784762661\n", + "CPU times: total: 26min 27s\n", + "Wall time: 5min 9s\n" ] }, { "data": { "text/html": [ - "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.87
95% Confidence interval for ATE (0.75, 1.02)
Estimated bias 0.0
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.02
untreated HistGradientBoostingRegressor 0.46
treated_cate HistGradientBoostingRegressor -0.02
untreated_cate HistGradientBoostingRegressor 0.46
propensity LogisticRegression 0.74
" + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 5000
Number of treated observations 1822
Average treatement effect (ATE) 0.14
95% Confidence interval for ATE (0.12, 0.15)
Estimated bias -0.0
Base learners
Model R^2
treated HistGradientBoostingRegressor 0.89
untreated HistGradientBoostingRegressor 0.85
treated_cate HistGradientBoostingRegressor 0.49
untreated_cate HistGradientBoostingRegressor 0.44
propensity LogisticRegression 0.64
" ], "text/plain": [ " ╔════════════════════════════════════════════════════════╗\n", @@ -367,15 +388,15 @@ " ╚════════════════════════════════════════════════════════╝\n", " \n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", - " ┃ Number of observations ┃ 100 │\n", + " ┃ Number of observations ┃ 5000 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Number of treated observations┃ 26 │\n", + " ┃ Number of treated observations┃ 1822 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃Average treatement effect (ATE)┃ 0.87 │\n", + " ┃Average treatement effect (ATE)┃ 0.14 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃95% Confidence interval for ATE┃ (0.75, 1.02) │\n", + " ┃95% Confidence interval for ATE┃ (0.12, 0.15) │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Estimated bias ┃ 0.0 │\n", + " ┃ Estimated bias ┃ -0.0 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", " ╔═══════════════╗\n", " ║ Base learners ║\n", @@ -384,21 +405,31 @@ " ╔═══════════════════════════════╦═══════════════════════════════╗\n", " ║ Model ║ R^2 ║\n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", - " ┃ treated ┃ HistGradientBoostingRegressor │ -0.02 │\n", + " ┃ treated ┃ HistGradientBoostingRegressor │ 0.89 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.46 │\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.85 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ treated_cate ┃ HistGradientBoostingRegressor │ -0.02 │\n", + " ┃ treated_cate ┃ HistGradientBoostingRegressor │ 0.49 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ untreated_cate ┃ HistGradientBoostingRegressor │ 0.46 │\n", + " ┃ untreated_cate ┃ HistGradientBoostingRegressor │ 0.44 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ propensity ┃ LogisticRegression │ 0.74 │\n", + " ┃ propensity ┃ LogisticRegression │ 0.64 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -420,16 +451,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Actual average treatement effect: 0.8455644128466571\n", - "MSE: 0.004913496827435016\n", - "CPU times: total: 3min 56s\n", - "Wall time: 38.1 s\n" + "Actual average treatement effect: 0.11803778255762709\n", + "MSE: 0.02027915744890722\n", + "CPU times: total: 25min 55s\n", + "Wall time: 4min 27s\n" ] }, { "data": { "text/html": [ - "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.87
95% Confidence interval for ATE (0.85, 1.01)
Estimated bias -0.01
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.07
untreated HistGradientBoostingRegressor 0.52
treated_cate HistGradientBoostingRegressor -0.05
untreated_cate HistGradientBoostingRegressor 0.52
propensity DummyClassifier 0.74
" + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 5000
Number of treated observations 1822
Average treatement effect (ATE) 0.13
95% Confidence interval for ATE (0.12, 0.15)
Estimated bias 0.0
Base learners
Model R^2
treated HistGradientBoostingRegressor 0.88
untreated HistGradientBoostingRegressor 0.85
treated_cate HistGradientBoostingRegressor 0.48
untreated_cate HistGradientBoostingRegressor 0.44
propensity DummyClassifier 0.64
" ], "text/plain": [ " ╔════════════════════════════════════════════════════════╗\n", @@ -437,15 +468,15 @@ " ╚════════════════════════════════════════════════════════╝\n", " \n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", - " ┃ Number of observations ┃ 100 │\n", + " ┃ Number of observations ┃ 5000 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Number of treated observations┃ 26 │\n", + " ┃ Number of treated observations┃ 1822 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃Average treatement effect (ATE)┃ 0.87 │\n", + " ┃Average treatement effect (ATE)┃ 0.13 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃95% Confidence interval for ATE┃ (0.85, 1.01) │\n", + " ┃95% Confidence interval for ATE┃ (0.12, 0.15) │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Estimated bias ┃ -0.01 │\n", + " ┃ Estimated bias ┃ 0.0 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", " ╔═══════════════╗\n", " ║ Base learners ║\n", @@ -454,21 +485,31 @@ " ╔═══════════════════════════════╦═══════════════════════════════╗\n", " ║ Model ║ R^2 ║\n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", - " ┃ treated ┃ HistGradientBoostingRegressor │ -0.07 │\n", + " ┃ treated ┃ HistGradientBoostingRegressor │ 0.88 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.52 │\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.85 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ treated_cate ┃ HistGradientBoostingRegressor │ -0.05 │\n", + " ┃ treated_cate ┃ HistGradientBoostingRegressor │ 0.48 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ untreated_cate ┃ HistGradientBoostingRegressor │ 0.52 │\n", + " ┃ untreated_cate ┃ HistGradientBoostingRegressor │ 0.44 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ propensity ┃ DummyClassifier │ 0.74 │\n", + " ┃ propensity ┃ DummyClassifier │ 0.64 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -511,16 +552,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Actual average treatement effect: 0.8455644128466571\n", - "MSE: 0.04738912217493377\n", - "CPU times: total: 2min 36s\n", - "Wall time: 27.1 s\n" + "Actual average treatement effect: 0.11803778255762709\n", + "MSE: 0.04268581526973136\n", + "CPU times: total: 22min 8s\n", + "Wall time: 3min 51s\n" ] }, { "data": { "text/html": [ - "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.91
95% Confidence interval for ATE (0.68, 1.46)
Estimated bias -0.12
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.01
untreated HistGradientBoostingRegressor -0.0
propensity DummyClassifier 0.74
pseudo_outcome HistGradientBoostingRegressor 0.02
" + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 5000
Number of treated observations 1822
Average treatement effect (ATE) 0.14
95% Confidence interval for ATE (0.12, 0.15)
Estimated bias -0.0
Base learners
Model R^2
treated HistGradientBoostingRegressor 0.84
untreated HistGradientBoostingRegressor 0.81
propensity HistGradientBoostingClassifier 0.77
pseudo_outcome HistGradientBoostingRegressor 0.1
" ], "text/plain": [ " ╔════════════════════════════════════════════════════════╗\n", @@ -528,15 +569,15 @@ " ╚════════════════════════════════════════════════════════╝\n", " \n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", - " ┃ Number of observations ┃ 100 │\n", + " ┃ Number of observations ┃ 5000 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Number of treated observations┃ 26 │\n", + " ┃ Number of treated observations┃ 1822 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃Average treatement effect (ATE)┃ 0.91 │\n", + " ┃Average treatement effect (ATE)┃ 0.14 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃95% Confidence interval for ATE┃ (0.68, 1.46) │\n", + " ┃95% Confidence interval for ATE┃ (0.12, 0.15) │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Estimated bias ┃ -0.12 │\n", + " ┃ Estimated bias ┃ -0.0 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", " ╔═══════════════╗\n", " ║ Base learners ║\n", @@ -545,19 +586,29 @@ " ╔═══════════════════════════════╦═══════════════════════════════╗\n", " ║ Model ║ R^2 ║\n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", - " ┃ treated ┃ HistGradientBoostingRegressor │ -0.01 │\n", + " ┃ treated ┃ HistGradientBoostingRegressor │ 0.84 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ untreated ┃ HistGradientBoostingRegressor │ -0.0 │\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.81 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ propensity ┃ DummyClassifier │ 0.74 │\n", + " ┃ propensity ┃ HistGradientBoostingClassifier│ 0.77 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ pseudo_outcome ┃ HistGradientBoostingRegressor │ 0.02 │\n", + " ┃ pseudo_outcome ┃ HistGradientBoostingRegressor │ 0.1 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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S7wEAAKSPsB0AACCB7OzsuPLKKxNft88++5R4N/EWpa08pma57rrrEl/TsGHDlPfcR0RMnDgx8vLytnpt9+7dU7bWXrp0aTz//POJanj00UdTAvrDDz889ttvv0T9VNRjjz2WEjQ2a9YshgwZkriv7373u3H88cdXVWk7jbp166YE3Pn5+fHuu+9Wqt9evXpF+/btE19X2usJKlvLjmrlypUpr5OIiPj5z39e4Z0lzjnnnJR3tD/11FPb/J0CAABUH2E7AABAAscff3w0bdq0QteeccYZKW3z58+PTz/9tLJlUU0OOeSQCofT3/ve9yI7O7tE29q1a2P27NlbvS4jI6PU1edJHswoKCgoNejbXqvaI6LU91R/73vfi7p161aov9K+P0QcdNBBKW1vv/12pfqs6FgfeOCBKW0LFiyoVC07queffz4lBG/Tpk0ce+yxFe6zTp06Kbuq5OfnV3q+AQCAihO2AwAAJFDaNsnl1aFDh9h3331T2ufMmVOZkqhGlZnvhg0bxjHHHJPSXp5gbMCAASlB/YsvvhiLFy8u172nT58en3/+eYm2xo0bV+rzJFFQUBBz585NaS9t5XN59ejRI+rXr1+ZsnZK317pHBHxySefVLi/rKysOOSQQyp07Z577pmyajs/Pz82btxY4Xp2VK+99lpK28knn1zpfo844oiUtm09wAMAAFSfrHQXAAAAsCMpbRVp0us/+uijEm1z5syJPn36VKpfqkdl5/vAAw9M2f69PA9XNGnSJE455ZSYMGFCcVthYWE88sgj8bOf/Wyb148dOzalrV+/fhXevjqpDz74INavX1+irXbt2tGhQ4cK97nl+jfeeKOy5dVYs2fPjtdffz3ef//9+PDDD2PlypWxbt26WLduXWzevLnc/axevbrCNbRo0SLq1atX4esbNGiQEq6vWbNmu/3s1RSvv/56SltpK/+TatmyZUrbvHnzKt0vAABQMcJ2AACAcmrQoEG0bt26Un106NAhnnjiiRJttpGvuSoTDpd1fXlXp59zzjklwvaIiHHjxsUll1wStWrVKvO6L774IqZPn57Svj23kC9tZfU+++yTslo/qY4dO+50YXteXl787W9/i8ceeywWLVpUJX2uWbOmwtc2atSoUvcu7TUBu9rK9k2bNsXChQtT2tevXx9vvfVWpfou7ffHqlWrKtUnAABQccJ2AACAcqrou9q31UdlgjGqT3Z2djRo0KBSfTRp0iSlrbyrjg877LBo3759iVWrS5YsiWnTpsUJJ5xQ5nWPPvpoFBQUlGg74ogjom3btuWsuvJK+4zNmjWrdL9V8R2sSV544YX49a9/nbLlf2V9+13hSVRmVXtZioqKqrzPmuyrr74qtf2aa66plvsJ2wEAIH28sx0AAKgxqjqQqer+quJ90aWFt4KSmqmyQXtERE5OTkpbkvk+55xzUtoefvjhMs8vLCyMRx99NKV9e65qjyj9AZKqGM+q6KOmmDx5clx88cVVHrSTftv7d7oHtgAAIH2sbAcAALa7sraS3rBhQ5Xe59vvjN7avcujtO2Rq6KPqv7cVI3atWtXuo/Sft7y8/PLff1pp50Wf/jDH2LdunXFbS+++GJ8/vnn0aJFi5Tzp0+fnhLeNm7cOE455ZQEVVdeaT/TVfHO7qr4DtYEc+fOjauuuiplB4KIiIyMjNhnn33i0EMPje985zvRvHnzyM3Njezs7KhTp05kZpZcN/Huu+/G8OHDt1fplEN5d6+oKoWFhdv1fgAAwP8jbAcAALa7hg0bltr+zUCxKpTWX2XeR1yZrZm3KK2mnWm17s6kKn4e165dm9JW2mr3stSvXz/69esXDz74YHFbQUFBPProo3HppZemnD927NiUtv79+1f6XelJlbYLRHV9f3ZEw4cPLzVoP+OMM2LIkCGxzz77lLuvqhhXqlZWln9uAwCAXYX/+gcAALa7unXrRnZ2dmzatKlEe2nBZGWU1l9ZQX9F+6uKPipTE9UnLy8vCgsLU1YSJ1Ha9s5J5/ucc84pEbZHRIwbNy4uvvjiqFWrVnHbkiVL4oUXXki5/qyzzkp0v6pQ2mesru9PumzevLlC173zzjsxe/bslPYbb7yx1NcGbIvXUNQ8ZT1ANXny5Gjbtu12rgYAAKhO3tkOAACkRWkrzD/++OMqvUdp/VVmZfsXX3yRaAvw0nz22WcpbZWpiepTWFgYn3zySaX6WLBgQUpb48aNE/XRrl27OPzww0u0ff755zF9+vQSbY8++mjKaukuXbqkJdwr7We6tJ/9pCrTxzcfTNiitNXl5bVy5coKXffvf/87pa1bt24VCtojIr766qsKXUf1ad68eant5goAAHY+wnYAACAt2rdvn9L2/vvvV1n/S5YsKXXFZ7t27SrcZ35+fsyfP78yZcV///vflLb999+/Un1Sfd57770qv75Dhw6J+yktiP3mlvGFhYUxbty4lHMGDhyY+F5VobSf6UWLFlV6ZXpl5qO0re0rsy39kiVLKnTdu+++m9LWr1+/Ctcxd+7cCl9L9WjQoEGpgfvixYvTUA0AAFCdhO0AAEBaHHrooSltn3zySSxbtqxK+n/jjTdKbT/ssMOqpd/yKCgoiLfeeiul/aCDDqpERTuOjIyMdJeQ2CuvvFKp62fOnJnS1rlz58T99OrVK5o0aVKibfr06cWB74svvhiLFi0qcbxx48bRq1evxPeqCq1atYrdd9+9RFtRUVG8/vrrFe5z5cqVlXrYJScnJ6Vt+fLlFe7vzTffrNB1pd2zMrsPVGZMqT6HHHJISturr766/QsBAACqlbAdAABIi29vix3x9ercp556qkr6nzRpUkpb7dq1Kx1sT5w4scLXvvzyyylBW0ZGRoXC1x1RdnZ2SltF33u9vUyZMiU2bdpUoWvnzJlT6jbypT1osi3Z2dnx/e9/v0RbQUFBPPLIIxFRcpX7FqeffnqpY769lPZzXZnvz1NPPVWp1zjsscceKW3z5s2rUF/r1q0r9UGK8ihtdX+9evUq1Ncbb7xR5a/fSKfSfl4r++qOdDn++ONT2l544YUK/z4BAABqJmE7AACQFocffng0a9YspX3s2LGVDlc+/fTTlPdZR0T07Nkz6tSpU6m+Z8+eHe+8806Frv3nP/+Z0nb00UdHbm5upWraUZS2jXdeXl4aKim/VatWFQfaSY0ZMyal7aCDDop99923Qv0NHDgwMjNL/jX+0UcfjS+++CJeeOGFlPPPOuusCt2nqpx66qkpbc8++2x88cUXifsqKCiIBx98sFL1HHDAASltb7zxRmzYsCFxX48++misWbOmQnU0aNAgpW3p0qUV6uuvf/1rha6rqUr7HbF+/fo0VFJ5J554YspDFMuWLSv1wRgAAGDHJWwHAADSIjs7O84777yU9g8//DDuvffeSvV94403lhrYDxo0qFL9bnHzzTdHUVFRomumT58e06ZNS2lPdyC6PTVs2DCl7dtbn9dEf/rTnxJvNz5jxox4+umnU9or8w711q1bR7du3Uq0ff7553HVVVel7BBw5JFHVjjUryqnnHJKNG7cuETbhg0b4ve//33ivv71r3/F+++/X6l62rdvH7Vr1y7Rtm7dupg8eXKifj799NMYNWpUhesobYV9aQ8HbcvEiRPjueeeq3AdNVGjRo1S2naE3xGladCgQam/32+//fZKvQ4BAACoWYTtAABA2px99tmlbp/8pz/9KR577LHE/RUWFsYNN9wQM2bMSDl26KGHVvp97VvMnj07fve735X7/AULFsS1116b0t6yZcs44YQTqqSmHcF+++2X0jZnzpzEDy5sb6tWrYoLL7yw1O2/S/Pf//43Lr/88pT25s2bR9++fStVy7nnnpvS9tprr6W0VSbUryp16tQpNWx88skn4/777y93P6+//nqFAvpvy87Ojp49e6a0jxw5Mr788sty9bF06dK4+OKLK7yqPSLiiCOOSGl76KGH4vPPPy93H7NmzYpf//rXFa6hpirt3fWzZ89OQyVVY9iwYSkPnKxevTqGDh1aJYH7ggUL4vHHH690PwAAQMUJ2wEAgLTJzc2N4cOHp7Rv3rw5fvGLX8QvfvGLcq9qfPPNN+OHP/xhPPzwwynH6tevH7feemul683IyCj+3/fee2/ceOON29wGfdasWTFo0KBYsWJFyrFf//rXKSttd2b77bdfyjuZly1bFv/617/SVNHW1alTp3jb9nfffTcGDhwYc+bM2eo1jz/+eJx//vmxevXqlGM33nhj7LbbbpWqqUePHtGyZcutnpObmxsnn3xype5TVYYNG1ZqvbfcckvccccdKSvyv23y5Mlx0UUXFW/1/u1t9JP69nvvI77+GfzRj34UCxYs2Oq1zz77bJx11lnFK+wrOpc9e/Ys8bsk4usV9kOGDIlPPvlkq9du2U5/6NChsW7duoiIqFWrVoXqqIk6deqU0vaPf/yjUg83pFPjxo3jlltuSWlftGhRfP/734+///3vibfJX7duXUyZMiUuuuiiOOWUU2LixIlVVS4AAFABWekuAAAA2LV973vfizfeeCMeeuihEu1FRUUxbty4mDBhQhx88MHRtWvXaNOmTTRq1Cjq1asXa9asiZUrV8bcuXNj5syZW91i+uabb4599tmn0rUeeuihsX79+njvvfci4uvVqC+88EJ8//vfj549e0aLFi2ifv36sXTp0pg7d25MnDgxnnnmmVJXbvft2zd69OhR6Zp2JLVr144TTjghnnrqqRLtN954Y7zwwgtx7LHHRuvWraN+/fopoWp2dnap79yuTk2bNo0TTjgh/vGPf0TE1684OOuss6JLly5xwgknROvWraNhw4axfPny+PDDD2Py5Mnx0UcfldrX9773vTj++OMrXVNmZmacddZZcdttt5V5zumnn57yUEO61KtXL37zm9/EkCFDSrQXFRXFqFGj4qmnnorvf//70b1792jevHlkZWXF0qVLY/bs2TF+/PiYOXNm8TW1atW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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -567,7 +618,7 @@ " y=y,\n", " treated=treated,\n", " model=HistGradientBoostingRegressor(),\n", - " propensity_score_model=DummyClassifier(),\n", + " propensity_score_model=HistGradientBoostingClassifier(),\n", ")\n", "\n", "summarize_learner(drlearner)\n", @@ -584,16 +635,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Actual average treatement effect: 0.8455644128466571\n", - "MSE: 0.03459741914128395\n", - "CPU times: total: 6min 14s\n", - "Wall time: 1min 6s\n" + "Actual average treatement effect: 0.11803778255762709\n", + "MSE: 0.024034992448110002\n", + "CPU times: total: 48min 25s\n", + "Wall time: 9min 13s\n" ] }, { "data": { "text/html": [ - "
Conditional Average Treatment Effect Estimator Summary
Number of observations 100
Number of treated observations 26
Average treatement effect (ATE) 0.95
95% Confidence interval for ATE (0.66, 1.33)
Estimated bias -0.1
Base learners
Model R^2
treated HistGradientBoostingRegressor -0.0
untreated HistGradientBoostingRegressor -0.07
propensity DummyClassifier 0.74
pseudo_outcome HistGradientBoostingRegressor -0.0
" + "
Conditional Average Treatment Effect Estimator Summary
Number of observations 5000
Number of treated observations 1822
Average treatement effect (ATE) 0.14
95% Confidence interval for ATE (0.12, 0.15)
Estimated bias -0.0
Base learners
Model R^2
treated HistGradientBoostingRegressor 0.85
untreated HistGradientBoostingRegressor 0.81
propensity HistGradientBoostingClassifier 0.77
pseudo_outcome HistGradientBoostingRegressor 0.1
" ], "text/plain": [ " ╔════════════════════════════════════════════════════════╗\n", @@ -601,15 +652,15 @@ " ╚════════════════════════════════════════════════════════╝\n", " \n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┱───────────────────────────────┐\n", - " ┃ Number of observations ┃ 100 │\n", + " ┃ Number of observations ┃ 5000 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Number of treated observations┃ 26 │\n", + " ┃ Number of treated observations┃ 1822 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃Average treatement effect (ATE)┃ 0.95 │\n", + " ┃Average treatement effect (ATE)┃ 0.14 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃95% Confidence interval for ATE┃ (0.66, 1.33) │\n", + " ┃95% Confidence interval for ATE┃ (0.12, 0.15) │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┤\n", - " ┃ Estimated bias ┃ -0.1 │\n", + " ┃ Estimated bias ┃ -0.0 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┘\n", " ╔═══════════════╗\n", " ║ Base learners ║\n", @@ -618,19 +669,29 @@ " ╔═══════════════════════════════╦═══════════════════════════════╗\n", " ║ Model ║ R^2 ║\n", " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╚═══════════════════════════════╩═══════════════════════════════╝ \n", - " ┃ treated ┃ HistGradientBoostingRegressor │ -0.0 │\n", + " ┃ treated ┃ HistGradientBoostingRegressor │ 0.85 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ untreated ┃ HistGradientBoostingRegressor │ -0.07 │\n", + " ┃ untreated ┃ HistGradientBoostingRegressor │ 0.81 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ propensity ┃ DummyClassifier │ 0.74 │\n", + " ┃ propensity ┃ HistGradientBoostingClassifier│ 0.77 │\n", " ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╋───────────────────────────────┼───────────────────────────────┤\n", - " ┃ pseudo_outcome ┃ HistGradientBoostingRegressor │ -0.0 │\n", + " ┃ pseudo_outcome ┃ HistGradientBoostingRegressor │ 0.1 │\n", " ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┹───────────────────────────────┴───────────────────────────────┘" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -640,7 +701,7 @@ " y=y,\n", " treated=treated,\n", " model=HistGradientBoostingRegressor(),\n", - " propensity_score_model=DummyClassifier(),\n", + " propensity_score_model=HistGradientBoostingClassifier(),\n", " cross_fitting=True,\n", ")\n", "\n", @@ -705,7 +766,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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