class_docstr: A Learner that learns a treatment assignment policy with observational data using doubly robust estimator of causal
  effect for binary treatment.
  
  Details of the policy learner are available at Athey and Wager (2018) (https://arxiv.org/abs/1702.02896).
class_docstr_tok: ["A","Learner","that","learns","a","treatment","assignment","policy","with","observational","data","using","doubly","robust","estimator","of","causal","effect","for","binary","treatment",".","Details","of","the","policy","learner","are","available","at","Athey","and","Wager","(","2018",")","(","https",":","\/\/arxiv.org\/abs\/1702.02896",")","."]
code: def __repr__(self):
      return (
          "{}(model_mu={},\n"
          "\tmodel_w={},\n"
          "\model_pi={})".format(
              self.__class__.__name__,
              self.model_mu.__repr__(),
              self.model_w.__repr__(),
              self.model_pi.__repr__(),
          )
      )
  
file_path: causalml/causalml/optimize/policylearner.py
imports: import logging
  import numpy
  from causalml.propensity import compute_propensity_score
  from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
  from sklearn.model_selection import cross_val_predict, KFold
  from sklearn.tree import DecisionTreeClassifier
label_desc: Use image node_id 2 for calling the PolicyLearner obj's underlying member method code with example usage: obj.__repr__() and returns: str