class_docstr: Base class for an estimator of causal effect.
  
  Subclasses implement different estimation methods. All estimation methods are in the package "dowhy.causal_estimators"
class_docstr_tok: ["Base","class","for","an","estimator","of","causal","effect",".","Subclasses","implement","different","estimation","methods",".","All","estimation","methods","are","in","the","package","``","dowhy.causal_estimators","''"]
code: def evaluate_effect_strength(self, data: pd.DataFrame, estimate):
      fraction_effect_explained = self._evaluate_effect_strength(
          data, estimate, method="fraction-effect"
      )
      # Need to test r-squared before supporting
      # effect_r_squared = self._evaluate_effect_strength(estimate, method="r-squared")
      strength_dict = {
          "fraction-effect": fraction_effect_explained
          #       'r-squared': effect_r_squared
      }
      return strength_dict
  
imports: import copy
  import logging
  from collections import namedtuple
  from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  import sympy
  from sklearn.utils import resample
  import dowhy.interpreters
  from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 21 for calling the CausalEstimator obj's underlying member method code with example usage: obj.evaluate_effect_strength(data, estimate) and returns: strength_dict