mcf_functions.ModifiedCausalForest.predict

ModifiedCausalForest.predict(data_df)

Compute all effects given a causal forest estimated with train() method.

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).

Returns

• results (Dictionary.) – Results. This dictionary has the following structure: ‘ate’: ATE, ‘ate_se’: Standard error of ATE, ‘ate effect_list’: List of names of estimated effects, ‘gate’: GATE, ‘gate_se’: SE of GATE, ‘gate_diff’: GATE minus ATE, ‘gate_diff_se’: Standard error of GATE minus ATE, ‘cbgate’: cbGATE (all covariates balanced), ‘cbgate_se’: Standard error of CBGATE, ‘cbgate_diff’: CBGATE minus ATE, ‘cbgate_diff_se’: Standard error of CBGATE minus ATE, ‘bgate’: BGATE (only prespecified covariates balanced), ‘bgate_se’: Standard error of BGATE, ‘bgate_diff’: BGATE minus ATE, ‘bgate_diff_se’: Standard errror of BGATE minus ATE, ‘gate_names_values’: Dictionary: Order of gates parameters and name and values of GATE effects. ‘iate’: IATE, ‘iate_se’: Standard error of IATE, ‘iate_eff’: (More) Efficient IATE (IATE estimated twice and averaged where role of tree_building and tree_filling sample is exchanged), ‘iate_data_df’: DataFrame with IATEs, ‘iate_names_dic’: Dictionary containing names of IATEs, ‘bala’: Effects of balancing tests, ‘bala_se’: Standard error of effects of balancing tests, ‘bala_effect_list’: Names of effects of balancing tests

• outpath (String) – Location of directory in which output is saved.