mcf_main.ModifiedCausalForest.predict_different_allocations

ModifiedCausalForest.predict_different_allocations(data_df, allocations_df: Optional[bool] = None)

Predict average potential outcomes for different allocations.

meth:~ModifiedCausalForest.train method must be run beforehand. The details of this methods are described in the working paper by Busshoff and Lechner (2025).

Parameters

• data_df (DataFrame) – Data used to compute the predictions. It must contain information about features (and treatment if effects for treatment specific subpopulations are desired as well).

• allocations_df (Dataframe or None, optional) – Different allocations which are to be evaluated. The length of this dataframe must be the same as the length of data_df. Default is None.

Returns

results – Results. This dictionary has the following structure: ‘ate’: Average treatment effects ‘ate_se’: Standard error of average treatment effects ‘ate_effect_list’: List with name with estiamted effects ‘alloc_df’: Dataframe with value and variance of value for all

allocations investigated.

’outpath’Pathlib object. Location of directory in which output

is saved.

Return type

Dictionary