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: Check partial rsqs values of feature corresponding confounding amonunt of ATE
  Args:
      sens_df (pandas.DataFrame): a data frame output from causalsens
      feature_name (str): feature name to check
      partial_rsqs_value (float) : partial rsquare value of feature
      range (float) : range to search from sens_df
  
  Return: min and max value of confounding amount
docstr_tok: ["Check","partial","rsqs","values","of","feature","corresponding","confounding","amonunt","of","ATE","Args",":","sens_df","(","pandas.DataFrame",")",":","a","data","frame","output","from","causalsens","feature_name","(","str",")",":","feature","name","to","check","partial_rsqs_value","(","float",")",":","partial","rsquare","value","of","feature","range","(","float",")",":","range","to","search","from","sens_df","Return",":","min","and","max","value","of","confounding","amount"]
code: def partial_rsqs_confounding(
      sens_df, feature_name, partial_rsqs_value, range=0.01
  ):
      """Check partial rsqs values of feature corresponding confounding amonunt of ATE
      Args:
          sens_df (pandas.DataFrame): a data frame output from causalsens
          feature_name (str): feature name to check
          partial_rsqs_value (float) : partial rsquare value of feature
          range (float) : range to search from sens_df
  
      Return: min and max value of confounding amount
      """
  
      rsqs_dict = []
      for i in sens_df.rsqs:
          if (
              partial_rsqs_value - partial_rsqs_value * range
              < i
              < partial_rsqs_value + partial_rsqs_value * range
          ):
              rsqs_dict.append(i)
  
      if rsqs_dict:
          confounding_min = sens_df[
              sens_df.rsqs.isin(rsqs_dict)
          ].alpha.min()
          confounding_max = sens_df[
              sens_df.rsqs.isin(rsqs_dict)
          ].alpha.max()
          logger.info(
              "Only works for linear outcome models right now. Check back soon."
          )
          logger.info(
              "For feature {} with partial rsquare {} confounding amount with possible values: {}, {}".format(
                  feature_name,
                  partial_rsqs_value,
                  confounding_min,
                  confounding_max,
              )
          )
          return [confounding_min, confounding_max]
      else:
          logger.info(
              "Cannot find correponding rsquare value within the range for input, please edit confounding",
              "values vector or use a larger range and try again",
          )
  
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 5 for calling the SensitivitySelectionBias obj's underlying member method code with example usage: obj.partial_rsqs_confounding(sens_df, feature_name, partial_rsqs_value, range) and returns: list