class_docstr: Add an unobserved confounder for refutation.
  
  AddUnobservedCommonCause class supports three methods:
      1) Simulation of an unobserved confounder
      2) Linear partial R2 : Sensitivity Analysis for linear models.
      3) Non-Parametric partial R2 based : Sensitivity Analyis for non-parametric models.
  
  Supports additional parameters that can be specified in the refute_estimate() method.
class_docstr_tok: ["Add","an","unobserved","confounder","for","refutation",".","AddUnobservedCommonCause","class","supports","three","methods",":","1",")","Simulation","of","an","unobserved","confounder","2",")","Linear","partial","R2",":","Sensitivity","Analysis","for","linear","models",".","3",")","Non-Parametric","partial","R2","based",":","Sensitivity","Analyis","for","non-parametric","models",".","Supports","additional","parameters","that","can","be","specified","in","the","refute_estimate","(",")","method","."]
code: def refute_estimate(self, show_progress_bar=False):
      if self.simulation_method == "linear-partial-R2":
          return sensitivity_linear_partial_r2(
              self._data,
              self._estimate,
              self._treatment_name,
              self.frac_strength_treatment,
              self.frac_strength_outcome,
              self.percent_change_estimate,
              self.benchmark_common_causes,
              self.significance_level,
              self.null_hypothesis_effect,
              self.plot_estimate,
          )
      elif self.simulation_method == "non-parametric-partial-R2":
          return sensitivity_non_parametric_partial_r2(
              self._estimate,
              self.kappa_t,
              self.kappa_y,
              self.frac_strength_treatment,
              self.frac_strength_outcome,
              self.benchmark_common_causes,
              self.plot_estimate,
              self.alpha_s_estimator_list,
              self.alpha_s_estimator_param_list,
              self.g_s_estimator_list,
              self.g_s_estimator_param_list,
              self.plugin_reisz,
          )
      elif self.simulation_method == "e-value":
          return sensitivity_e_value(
              self._data,
              self._target_estimand,
              self._estimate,
              self._treatment_name,
              self._outcome_name,
              self.plot_estimate,
          )
      elif self.simulation_method == "direct-simulation":
          refute = sensitivity_simulation(
              self._data,
              self._target_estimand,
              self._estimate,
              self._treatment_name,
              self._outcome_name,
              self.kappa_t,
              self.kappa_y,
              self.effect_on_t,
              self.effect_on_y,
              self.frac_strength_treatment,
              self.frac_strength_outcome,
              self.plotmethod,
              show_progress_bar,
          )
          refute.add_refuter(self)
          return refute
  
imports: import copy
  import logging
  import math
  from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  import scipy.stats
  import statsmodels.api
  from sklearn.linear_model import LogisticRegression
  from sklearn.preprocessing import StandardScaler
  from tqdm.auto import tqdm
  from dowhy.causal_estimator import CausalEstimate
  from dowhy.causal_estimators.econml import Econml
  from dowhy.causal_estimators.linear_regression_estimator import LinearRegressionEstimator
  from dowhy.causal_estimators.regression_estimator import RegressionEstimator
  from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
  from dowhy.causal_refuter import CausalRefutation, CausalRefuter, choose_variables
  from dowhy.causal_refuters.evalue_sensitivity_analyzer import EValueSensitivityAnalyzer
  from dowhy.causal_refuters.linear_sensitivity_analyzer import LinearSensitivityAnalyzer
label_desc: Use image node_id 2 for calling the AddUnobservedCommonCause obj's underlying member method code with example usage: obj.refute_estimate(show_progress_bar) and returns: sensitivity_linear_partial_r2, sensitivity_non_parametric_partial_r2, sensitivity_e_value, refute