class_docstr: Manager for generating causal analyses from a dataset.
class_docstr_tok: ["Manager","for","generating","causal","analyses","from","a","dataset","."]
code: def _fit_causal_analysis(
      self, causal_analysis, X, y, max_cat_expansion
  ):
      try:
          causal_analysis.fit(X, y)
      except ValueError as e:
          message = str(e)
          expected = "increase the upper_bound_on_cat_expansion"
          clarification = (
              " Increase the value {} in rai_insights.causal.add("
              "upper_bound_on_cat_expansion={})."
          ).format(max_cat_expansion, max_cat_expansion)
          if expected in message:
              raise UserConfigValidationException(
                  message + clarification
              )
          raise e
  
imports: from pathlib import Path
  from typing import Any, List, Optional, Union
  import numpy
  import pandas
  from econml.solutions.causal_analysis import CausalAnalysis
  from raiutils.exceptions import UserConfigValidationException
  from raiutils.models import ModelTask
  from responsibleai._data_validations import validate_train_test_categories
  from responsibleai._internal.constants import CausalManagerKeys, ListProperties, ManagerNames
  from responsibleai._tools.causal.causal_config import CausalConfig
  from responsibleai._tools.causal.causal_constants import DefaultParams, ModelTypes
  from responsibleai._tools.causal.causal_result import CausalResult
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai.managers.base_manager import BaseManager
  from responsibleai.utils import _find_features_having_missing_values, _measure_time
label_desc: Use image node_id 3 for calling the CausalManager obj's underlying member method code with example usage: obj._fit_causal_analysis(causal_analysis, X, y, max_cat_expansion) without return types