class_docstr: A Sensitivity Check class to support Placebo Treatment, Irrelevant Additional Confounder
  and Subset validation refutation methods to verify causal inference.
  
  Reference: https://github.com/microsoft/dowhy/blob/master/dowhy/causal_refuters/
docstr: Summary report
  Args:
      method_name (str): sensitivity analysis method
  
  Returns:
      (pd.DataFrame): a summary dataframe
docstr_tok: ["Summary","report","Args",":","method_name","(","str",")",":","sensitivity","analysis","method","Returns",":","(","pd.DataFrame",")",":","a","summary","dataframe"]
code: def summary(self, method):
      """Summary report
      Args:
          method_name (str): sensitivity analysis method
  
      Returns:
          (pd.DataFrame): a summary dataframe
      """
      method_name = method
  
      X = self.df[self.inference_features].values
      p = self.df[self.p_col].values
      treatment = self.df[self.treatment_col].values
      y = self.df[self.outcome_col].values
  
      preds = self.get_prediction(X, p, treatment, y)
      ate = preds.mean()
      (
          ate_new,
          ate_new_lower,
          ate_new_upper,
      ) = self.sensitivity_estimate()
  
      sensitivity_summary = pd.DataFrame(
          [method_name, ate, ate_new, ate_new_lower, ate_new_upper]
      ).T
      sensitivity_summary.columns = SUMMARY_COLS
      return sensitivity_summary
  
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 6 for calling the Sensitivity obj's underlying member method code with example usage: obj.summary(method) and returns: sensitivity_summary