docstr: Initialize a DR-learner with two XGBoost models.
docstr_tok: ["Initialize","a","DR-learner","with","two","XGBoost","models","."]
code: def __init__(self, ate_alpha=0.05, control_name=0, *args, **kwargs):
      """Initialize a DR-learner with two XGBoost models."""
      super().__init__(
          learner=XGBRegressor(*args, **kwargs),
          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 XGBDRRegressor object from inherited base classes: BaseDRRegressor with example: obj = XGBDRRegressor(ate_alpha, control_name)