mcf_main.ModifiedCausalForest.train

ModifiedCausalForest.train(data_df, exit_after_commonsupport=False)

Build the modified causal forest on the training data.

Parameters:

• data_df (DataFrame) – Data used to compute the causal forest. It must contain information about outcomes, treatment, and features.

• exit_after_commonsupport (Boolean) – Programme exits once the common support is determined. This is useful to determine common support cut-offs only (that can subsequently be used by the predict method).

Returns:

results – Contains the results. This dictionary has the following structure: … : Values and names of the effects

’inputdata_on_support’DataFrame or None

Data that are inside common support.

’common_support_probabilities’: DataFrame or None

Treatment probabilities for all treatments, the identifier of the observation, and a dummy variable indicating whether the observation is inside or outside the common support. This is for the data used to build the trees. None if _int_with_output is False or no common support enforcement.

’path_output’Pathlib object

Location of directory in which output is saved.

Return type:

Dictionary.