mcf_main.ModifiedCausalForest.predict

ModifiedCausalForest.predict(data_df)

Compute all effects.

meth:~ModifiedCausalForest.train method must be run beforehand.

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 – Contains the 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. ‘qiate’: QIATE, ‘qiate_se’: Standard error of QIATEs, ‘qiate_diff’: QIATE minus QIATE at median, ‘qiate_diff_se’: Standard error of QIATE minus QIATE at median, ‘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. ‘common_support_probabilities’ : pd.DataFrame containing 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. None if _int_with_output is False. ‘path_output’: Pathlib object, location of directory in which output is saved.

Return type

Dictionary.