class_docstr: A parent class for S-learner regressor classes.
class_docstr_tok: ["A","parent","class","for","S-learner","regressor","classes","."]
docstr: Initialize an S-learner regressor.
  Args:
      learner (optional): a model to estimate the treatment effect
      control_name (str or int, optional): name of control group
docstr_tok: ["Initialize","an","S-learner","regressor",".","Args",":","learner","(","optional",")",":","a","model","to","estimate","the","treatment","effect","control_name","(","str","or","int",",","optional",")",":","name","of","control","group"]
code: def __init__(self, learner=None, ate_alpha=0.05, control_name=0):
      """Initialize an S-learner regressor.
      Args:
          learner (optional): a model to estimate the treatment effect
          control_name (str or int, optional): name of control group
      """
      super().__init__(
          learner=learner,
          ate_alpha=ate_alpha,
          control_name=control_name,
      )
  
imports: import logging
  import numpy
  from tqdm import tqdm
  from scipy.stats import norm
  from sklearn.dummy import DummyRegressor
  import statsmodels.api
  from copy import deepcopy
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, convert_pd_to_np
  from causalml.metrics import regression_metrics, classification_metrics
label_desc: Use image node_id 1 to create a new BaseSRegressor object from inherited base classes: BaseSLearner with example: obj = BaseSRegressor(learner, ate_alpha, control_name)