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",")","."]
docstr: Predict treatment assignment that optimizes the outcome.
  
  Args:
      X (np.matrix): a feature matrix
  
  Returns:
      (numpy.ndarray): predictions of treatment assignment.
docstr_tok: ["Predict","treatment","assignment","that","optimizes","the","outcome",".","Args",":","X","(","np.matrix",")",":","a","feature","matrix","Returns",":","(","numpy.ndarray",")",":","predictions","of","treatment","assignment","."]
code: def predict(self, X):
      """Predict treatment assignment that optimizes the outcome.
  
      Args:
          X (np.matrix): a feature matrix
  
      Returns:
          (numpy.ndarray): predictions of treatment assignment.
      """
  
      return self.model_pi.predict(X)
  
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 6 for calling the PolicyLearner obj's underlying member method code with example usage: obj.predict(X) and returns: self