class_docstr: Reference:
  
  [1] Blackwell, Matthew. "A selection bias approach to sensitivity analysis
  for causal effects." Political Analysis 22.2 (2014): 169-182.
  https://www.mattblackwell.org/files/papers/causalsens.pdf
  
  [2] Confouding parameter alpha_range using the same range as in:
  https://github.com/mattblackwell/causalsens/blob/master/R/causalsens.R
code: def __init__(
      self,
      *args,
      confound="one_sided",
      alpha_range=None,
      sensitivity_features=None,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
      """Initialize.
  
          Args:
              confound (string): the name of confouding function
              alpha_range (np.array): a parameter to pass the confounding function
              sensitivity_features (list of str): ): a list of columns that to check each individual partial r-square
          """
  
      logger.info(
          "Only works for linear outcome models right now. Check back soon."
      )
      confounding_functions = {
          "one_sided": one_sided,
          "alignment": alignment,
          "one_sided_att": one_sided_att,
          "alignment_att": alignment_att,
      }
  
      try:
          confound_func = confounding_functions[confound]
      except KeyError:
          raise NotImplementedError(
              f"Confounding function, {confound} is not implemented. \
                                          Use one of {confounding_functions.keys()}"
          )
  
      self.confound = confound_func
  
      if sensitivity_features is None:
          self.sensitivity_features = self.inference_features
      else:
          self.sensitivity_features = sensitivity_features
  
      if alpha_range is None:
          y = self.df[self.outcome_col]
          iqr = y.quantile(0.75) - y.quantile(0.25)
          self.alpha_range = np.linspace(-iqr / 2, iqr / 2, 11)
          if 0 not in self.alpha_range:
              self.alpha_range = np.append(self.alpha_range, 0)
      else:
          self.alpha_range = alpha_range
  
      self.alpha_range.sort()
  
imports: import logging
  import numpy
  import pandas
  from collections import defaultdict
  import matplotlib.pyplot
  from importlib import import_module
label_desc: Use image node_id 1 to create a new SensitivitySelectionBias object from inherited base classes: Sensitivity with example: obj = SensitivitySelectionBias()