class_docstr: A parent class for DR-learner regressor classes.
class_docstr_tok: ["A","parent","class","for","DR-learner","regressor","classes","."]
docstr: Initialize an DR-learner regressor.
  
  Args:
      learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment
          groups
      control_outcome_learner (optional): a model to estimate outcomes in the control group
      treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group
      control_effect_learner (optional): a model to estimate treatment effects in the control group
      treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group
      ate_alpha (float, optional): the confidence level alpha of the ATE estimate
      control_name (str or int, optional): name of control group
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code: def __init__(
      self,
      learner=None,
      control_outcome_learner=None,
      treatment_outcome_learner=None,
      treatment_effect_learner=None,
      ate_alpha=0.05,
      control_name=0,
  ):
      """Initialize an DR-learner regressor.
  
      Args:
          learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment
              groups
          control_outcome_learner (optional): a model to estimate outcomes in the control group
          treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group
          control_effect_learner (optional): a model to estimate treatment effects in the control group
          treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group
          ate_alpha (float, optional): the confidence level alpha of the ATE estimate
          control_name (str or int, optional): name of control group
      """
      super().__init__(
          learner=learner,
          control_outcome_learner=control_outcome_learner,
          treatment_outcome_learner=treatment_outcome_learner,
          treatment_effect_learner=treatment_effect_learner,
          ate_alpha=ate_alpha,
          control_name=control_name,
      )
  
imports: from copy import deepcopy
  import logging
  import numpy
  import pandas
  from scipy.stats import norm
  from sklearn.model_selection import cross_val_predict, KFold
  from tqdm import tqdm
  from xgboost import XGBRegressor
  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.metrics import regression_metrics, classification_metrics
  from causalml.propensity import compute_propensity_score
label_desc: Use image node_id 1 to create a new BaseDRRegressor object from inherited base classes: BaseDRLearner with example: obj = BaseDRRegressor(learner, control_outcome_learner, treatment_outcome_learner, treatment_effect_learner, ate_alpha, control_name)