class_docstr: Class for the estimate object that every causal estimator returns
class_docstr_tok: ["Class","for","the","estimate","object","that","every","causal","estimator","returns"]
docstr: Estimate treatment effect conditioned on given variables.
  
  If a numeric effect modifier is provided, it is discretized into quantile bins. If you would like a custom discretization, you can do so yourself: create a new column containing the discretized effect modifier and then include that column's name in the effect_modifier_names argument.
  
  :param effect_modifiers: Names of effect modifier variables over which the conditional effects will be estimated. If not provided, defaults to the effect modifiers specified during creation of the CausalEstimator object.
  :param num_quantiles: The number of quantiles into which a numeric effect modifier variable is discretized. Does not affect any categorical effect modifiers.
  
  :returns: A (multi-index) dataframe that provides separate effects for each value of the (discretized) effect modifiers.
code: def estimate_conditional_effects(
      self,
      effect_modifiers=None,
      num_quantiles=CausalEstimator.NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS,
  ):
      """Estimate treatment effect conditioned on given variables.
  
      If a numeric effect modifier is provided, it is discretized into quantile bins. If you would like a custom discretization, you can do so yourself: create a new column containing the discretized effect modifier and then include that column's name in the effect_modifier_names argument.
  
      :param effect_modifiers: Names of effect modifier variables over which the conditional effects will be estimated. If not provided, defaults to the effect modifiers specified during creation of the CausalEstimator object.
      :param num_quantiles: The number of quantiles into which a numeric effect modifier variable is discretized. Does not affect any categorical effect modifiers.
  
      :returns: A (multi-index) dataframe that provides separate effects for each value of the (discretized) effect modifiers.
      """
      return self.estimator._estimate_conditional_effects(
          self.estimator._estimate_effect_fn,
          effect_modifiers,
          num_quantiles,
      )
  
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 8 for calling the CausalEstimate obj's underlying member method code with example usage: obj.estimate_conditional_effects(effect_modifiers, num_quantiles) and returns: self