causalkit.inference.causalforestdml#

causalkit.inference.causalforestdml(data, model_y=None, model_t=None, n_estimators=100, max_depth=None, min_samples_leaf=5, cv=5, n_jobs=-1, random_state=None, confidence_level=0.95)[source]#

Estimate average treatment effects using EconML’s CausalForestDML.

Parameters:

  • data (CausalData) – The causaldata object containing treatment, target, and confounders variables.

  • model_y (estimator, optional) – The model for fitting the outcome variable. If None, a CatBoostRegressor configured to use all CPU cores is used.

  • model_t (estimator, optional) – The model for fitting the treatment variable. If None, a CatBoostRegressor configured to use all CPU cores is used.

  • n_estimators (int, default 100) – Number of trees in the forest.

  • max_depth (int, optional) – Maximum depth of the trees. If None, nodes are expanded until all leaves are pure or contain less than min_samples_leaf samples.

  • min_samples_leaf (int, default 5) – Minimum number of samples required to be at a leaf node.

  • cv (int, default 5) – Number of folds for cross-fitting.

  • n_jobs (int, default -1) – Number of jobs to run in parallel. -1 means using all processors.

  • random_state (int, optional) – Controls the randomness of the estimator.

  • confidence_level (float, default 0.95) – The confidence level for calculating confidence intervals (between 0 and 1).

Returns:

A dictionary containing: - coefficient: The estimated average treatment effect - std_error: The standard error of the estimate - p_value: The p-value for the null hypothesis that the effect is zero - confidence_interval: Tuple of (lower, upper) bounds for the confidence interval - model: The fitted CausalForestDML object

Return type:

Dict[str, Any]

Raises:

ValueError – If the causaldata object doesn’t have treatment, target, and confounders variables defined, or if the treatment variable is not binary.

Examples

>>> from causalkit.data import generate_rct_data
>>> from causalkit.data import CausalData
>>> from causalkit.inference.ate import causalforestdml
>>>
>>> # Generate data
>>> df = generate_rct_data()
>>>
>>> # Create causaldata object
>>> ck = CausalData(
...     df=df,
...     outcome='outcome',
...     treatment='treatment',
...     confounders=['age', 'invited_friend']
... )
>>>
>>> # Estimate ATE using CausalForestDML
>>> results = causalforestdml(ck)
>>> print(f"ATE: {results['coefficient']:.4f}")
>>> print(f"Standard Error: {results['std_error']:.4f}")
>>> print(f"P-value: {results['p_value']:.4f}")
>>> print(f"Confidence Interval: {results['confidence_interval']}")
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