class_docstr: A parent class for X-learner regressor classes.
  
  An X-learner estimates treatment effects with four machine learning models.
  
  Details of X-learner are available at Kunzel et al. (2018) (https://arxiv.org/abs/1706.03461).
class_docstr_tok: ["A","parent","class","for","X-learner","regressor","classes",".","An","X-learner","estimates","treatment","effects","with","four","machine","learning","models",".","Details","of","X-learner","are","available","at","Kunzel","et","al",".","(","2018",")","(","https",":","\/\/arxiv.org\/abs\/1706.03461",")","."]
code: def __repr__(self):
      return (
          "{}(control_outcome_learner={},\n"
          "\ttreatment_outcome_learner={},\n"
          "\tcontrol_effect_learner={},\n"
          "\ttreatment_effect_learner={})".format(
              self.__class__.__name__,
              self.model_mu_c.__repr__(),
              self.model_mu_t.__repr__(),
              self.model_tau_c.__repr__(),
              self.model_tau_t.__repr__(),
          )
      )
  
imports: from copy import deepcopy
  import logging
  import numpy
  import pandas
  from tqdm import tqdm
  from scipy.stats import norm
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, check_p_conditions, convert_pd_to_np
  from causalml.inference.meta.explainer import Explainer
  from causalml.metrics import regression_metrics, classification_metrics
  from causalml.propensity import compute_propensity_score
label_desc: Use image node_id 2 for calling the BaseXLearner obj's underlying member method code with example usage: obj.__repr__() and returns: str