causalis.scenarios.classic_rct.model

Module Contents

Classes

DiffInMeans

Difference-in-means model for CausalData. Wraps common RCT inference methods: t-test, bootstrap, and conversion z-test.

API

class causalis.scenarios.classic_rct.model.DiffInMeans

Difference-in-means model for CausalData. Wraps common RCT inference methods: t-test, bootstrap, and conversion z-test.

Initialization

fit(data: causalis.dgp.causaldata.CausalData) → causalis.scenarios.classic_rct.model.DiffInMeans

Fit the model by storing the CausalData object.

Parameters

data : CausalData The CausalData object containing treatment and outcome variables.

Returns

DiffInMeans The fitted model.

estimate(method: Literal[causalis.scenarios.classic_rct.inference.ttest, bootstrap, causalis.scenarios.classic_rct.inference.conversion_ztest] = 'ttest', alpha: float = 0.05, diagnostic_data: bool = True, **kwargs: Any) → causalis.data_contracts.causal_estimate.CausalEstimate

Compute the treatment effect using the specified method.

Parameters

method : {“ttest”, “bootstrap”, “conversion_ztest”}, default “ttest” The inference method to use. - “ttest”: Standard independent two-sample t-test. - “bootstrap”: Bootstrap-based inference for difference in means. - “conversion_ztest”: Two-proportion z-test for binary outcomes. alpha : float, default 0.05 The significance level for calculating confidence intervals. diagnostic_data : bool, default True Whether to include diagnostic data_contracts in the result. **kwargs : Any Additional arguments passed to the underlying inference function. - For “bootstrap”: can pass n_simul, batch_size, seed, index_dtype.

Returns

CausalEstimate A results object containing effect estimates and inference.

__repr__() → str