R-Learner

XGBRRegressor

 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)

A parent class for R-learner regressor classes.

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BaseRClassifier

 BaseRClassifier (outcome_learner=None, effect_learner=None,
                  propensity_learner=LogisticRegressionCV(Cs=array([1.0023
                  0524, 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)

A parent class for R-learner classifier classes.

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BaseRRegressor

 BaseRRegressor (learner=None, outcome_learner=None, effect_learner=None,
                 propensity_learner=LogisticRegressionCV(Cs=array([1.00230
                 524, 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)

A parent class for R-learner regressor classes.

source

BaseRLearner

 BaseRLearner (learner=None, outcome_learner=None, effect_learner=None,
               propensity_learner=LogisticRegressionCV(Cs=array([1.0023052
               4, 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)

*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).*