docstr: Initialize an S-learner with a linear regression model.
  Args:
      ate_alpha (float, optional): the confidence level alpha of the ATE estimate
      control_name (str or int, optional): name of control group
docstr_tok: ["Initialize","an","S-learner","with","a","linear","regression","model",".","Args",":","ate_alpha","(","float",",","optional",")",":","the","confidence","level","alpha","of","the","ATE","estimate","control_name","(","str","or","int",",","optional",")",":","name","of","control","group"]
code: def __init__(self, ate_alpha=0.05, control_name=0):
      """Initialize an S-learner with a linear regression model.
      Args:
          ate_alpha (float, optional): the confidence level alpha of the ATE estimate
          control_name (str or int, optional): name of control group
      """
      super().__init__(
          StatsmodelsOLS(alpha=ate_alpha), ate_alpha, 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 LRSRegressor object from inherited base classes: BaseSRegressor with example: obj = LRSRegressor(ate_alpha, control_name)