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
docstr: Summary report for Selection Bias Method
  Args:
      method_name (str): sensitivity analysis method
  Returns:
      (pd.DataFrame): a summary dataframe
docstr_tok: ["Summary","report","for","Selection","Bias","Method","Args",":","method_name","(","str",")",":","sensitivity","analysis","method","Returns",":","(","pd.DataFrame",")",":","a","summary","dataframe"]
code: def summary(self, method="Selection Bias"):
      """Summary report for Selection Bias Method
      Args:
          method_name (str): sensitivity analysis method
      Returns:
          (pd.DataFrame): a summary dataframe
      """
  
      method_name = method
      sensitivity_summary = self.causalsens()[0]
      sensitivity_summary["Method"] = [
          method_name
          + " (alpha@"
          + str(round(i, 5))
          + ", with r-sqaure:"
          for i in sensitivity_summary.alpha
      ]
      sensitivity_summary["Method"] = sensitivity_summary[
          "Method"
      ] + sensitivity_summary["rsqs"].round(5).astype(str)
      sensitivity_summary["ATE"] = sensitivity_summary[
          sensitivity_summary.alpha == 0
      ]["New ATE"]
      return sensitivity_summary[SUMMARY_COLS]
  
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 3 for calling the SensitivitySelectionBias obj's underlying member method code with example usage: obj.summary(method) and returns: sensitivity_summary