class_docstr: Manager for generating causal analyses from a dataset.
class_docstr_tok: ["Manager","for","generating","causal","analyses","from","a","dataset","."]
code: def _create_policy(
      self,
      causal_result,
      X_test,
      treatment_feature,
      treatment_cost,
      alpha,
      max_tree_depth,
      min_tree_leaf_samples,
  ):
      return causal_result._create_policy(
          X_test=X_test,
          treatment_feature=treatment_feature,
          treatment_cost=treatment_cost,
          alpha=alpha,
          max_tree_depth=max_tree_depth,
          min_tree_leaf_samples=min_tree_leaf_samples,
      )
  
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 4 for calling the CausalManager obj's underlying member method code with example usage: obj._create_policy(causal_result, X_test, treatment_feature, treatment_cost, alpha, max_tree_depth, min_tree_leaf_samples) and returns: causal_result