class_docstr: Manager for generating causal analyses from a dataset.
class_docstr_tok: ["Manager","for","generating","causal","analyses","from","a","dataset","."]
docstr: Get what-if data.
docstr_tok: ["Get","what-if","data","."]
code: def _whatif(self, id, X, X_feature_new, feature_name, y, alpha=0.1):
      """Get what-if data."""
      filtered = [r for r in self.get() if r.id == id]
      if len(filtered) == 0:
          raise ValueError(
              f"Failed to find causal result with ID: {id}"
          )
      result = filtered[0]
      return result._whatif(
          X, X_feature_new, feature_name, y, alpha=alpha
      ).to_dict(orient="records")
  
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 5 for calling the CausalManager obj's underlying member method code with example usage: obj._whatif(id, X, X_feature_new, feature_name, y, alpha) and returns: result