class BaseRLearner[source]

BaseRLearner(learner=None, outcome_learner=None, effect_learner=None, propensity_learner=LogisticRegressionCV(Cs=array([1.00230524, 2.15608891, 4.63802765, 9.97700064]), cv=StratifiedKFold(n_splits=4, random_state=42, shuffle=True), l1_ratios=array([0.001 , 0.33366667, 0.66633333, 0.999 ]), penalty='elasticnet', random_state=42, solver='saga'), ate_alpha=0.05, control_name=0, n_fold=5, random_state=None) :: BaseLearner

A parent class for R-learner classes.

An R-learner estimates treatment effects with two machine learning models and the propensity score.

Details of R-learner are available at Nie and Wager (2019) (https://arxiv.org/abs/1712.04912).

class BaseRRegressor[source]

BaseRRegressor(learner=None, outcome_learner=None, effect_learner=None, propensity_learner=LogisticRegressionCV(Cs=array([1.00230524, 2.15608891, 4.63802765, 9.97700064]), cv=StratifiedKFold(n_splits=4, random_state=42, shuffle=True), l1_ratios=array([0.001 , 0.33366667, 0.66633333, 0.999 ]), penalty='elasticnet', random_state=42, solver='saga'), ate_alpha=0.05, control_name=0, n_fold=5, random_state=None) :: BaseRLearner

A parent class for R-learner regressor classes.

class BaseRClassifier[source]

BaseRClassifier(outcome_learner=None, effect_learner=None, propensity_learner=LogisticRegressionCV(Cs=array([1.00230524, 2.15608891, 4.63802765, 9.97700064]), cv=StratifiedKFold(n_splits=4, random_state=42, shuffle=True), l1_ratios=array([0.001 , 0.33366667, 0.66633333, 0.999 ]), penalty='elasticnet', random_state=42, solver='saga'), ate_alpha=0.05, control_name=0, n_fold=5, random_state=None) :: BaseRLearner

A parent class for R-learner classifier classes.

class XGBRRegressor[source]

XGBRRegressor(early_stopping=True, test_size=0.3, early_stopping_rounds=30, effect_learner_objective='rank:pairwise', effect_learner_n_estimators=500, random_state=42, *args, **kwargs) :: BaseRRegressor

A parent class for R-learner regressor classes.