mcf_main.ModifiedCausalForest.predict#

ModifiedCausalForest.predict(data_df, new_keywords=None, exit_after_commonsupport=False)#

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).

  • new_keywords (Dictionary (or None). Default is None.) –

    Parameters of mcf instance to be changed. The keys in the dictionary are the parameters to be changed when running this method (and all methods that are run subsequently, like analyse or sensitivity), and the values corresponding to the keys are the new value (None is not allowed as new value). However, not all parameters can differ from those used during training. The following parameters can be changed, and thus specified as keys in this dictionary (some of these will however not influence the results of this method, but of other other used with the same instance): ‘cs_type’ (can only be changed to 0, use only if prediction data are already on common

    support);

    ’gen_output_type’; ‘var_x_name_balance_test_ord’, ‘var_x_name_balance_test_unord’, ‘var_x_name_balance_bgate’, ‘var_x_name_ba’, ‘var_z_name_ord’, ‘var_z_name_unord’, ‘p_ba’, ‘p_ba_adj_method’, ‘p_ba_pos_weights_only’, ‘p_ba_use_x’, ‘p_ba_use_prop_score’, ‘p_ba_use_prog_score’, ‘p_ba_ridge’, ‘p_ate_no_se_only’, ‘p_atet’, ‘p_gatet’, ‘p_bgate’, ‘p_cbgate’, ‘p_iate’, ‘p_iate_se’, ‘p_iate_m_ate’, ‘p_bgate_sample_share’, ‘p_gates_minus_previous’, ‘p_gates_smooth_bandwidth’, ‘p_gates_smooth’, ‘p_gates_smooth_no_evalu_points’, ‘p_gates_no_evalu_points’, ‘p_qiate’, ‘p_qiate_se’, ‘p_qiate_m_mqiate’, ‘p_qiate_m_opp’, ‘p_qiate_no_of_quantiles’, ‘p_qiate_smooth’, ‘p_qiate_smooth_bandwidth’, ‘p_qiate_bias_adjust’, ‘p_bt_yes’, ‘p_choice_based_sampling’, ‘p_choice_based_probs’, ‘p_cond_var’, ‘p_knn’, ‘p_knn_const’, ‘p_knn_min_k’, ‘p_nw_bandw’, ‘p_nw_kern’, ‘p_ci_level’, ‘p_se_boot_ate’, ‘p_se_boot_gate’, ‘p_se_boot_iate’, ‘p_se_boot_qiate’; ‘post_bin_corr_threshold’, ‘post_bin_corr_yes’, ‘post_est_stats’, ‘post_kmeans_yes’, ‘post_kmeans_no_of_groups’, ‘post_kmeans_max_tries’, ‘post_kmeans_min_size_share’, ‘post_kmeans_replications’, ‘post_kmeans_single’, ‘post_random_forest_vi’, ‘post_relative_to_first_group_only’, ‘post_plots’, ‘post_tree’.

    There is also a key in this dictionary that, if specified as True will turn off version prediction even if it has been used during training: ‘no_treatment_versions’.

  • exit_after_commonsupport (Boolean, optional) – Programme exits once the common support is determined. This is useful to determine common support cut-offs only.

Returns:

results – Contains the results. This dictionary has the following structure: ‘ate’: ATE, ‘ate_se’: Standard error of ATE. None if exit_after_commonsupport is False. ‘ate_effect_list’: List of names of estimated effects. None if exit_after_commonsupport

is False.

’gate’: GATE, ‘gate_se’: SE of GATE. None if exit_after_commonsupport is False. ‘gate_diff’: GATE minus ATE. None if exit_after_commonsupport is False. ‘gate_diff_se’: Standard error of GATE minus ATE. None if exit_after_commonsupport is

False.

’cbgate’: cbGATE (all covariates balanced). None if exit_after_commonsupport is False. ‘cbgate_se’: Standard error of CBGATE. None if exit_after_commonsupport is False. ‘cbgate_diff’: CBGATE minus ATE. None if exit_after_commonsupport is False. ‘cbgate_diff_se’: Standard error of CBGATE minus ATE. None if exit_after_commonsupport

is False.

’bgate’: BGATE (only prespecified covariates balanced). None if exit_after_commonsupport

is False.

’bgate_se’: Standard error of BGATE. None if exit_after_commonsupport is False. ‘bgate_diff’: BGATE minus ATE. None if exit_after_commonsupport is False. ‘bgate_diff_se’: Standard errror of BGATE minus ATE. None if exit_after_commonsupport

is False.

’gate_names_values’: Dictionary: Order of gates parameters and name and values of GATE

effects.

None if exit_after_commonsupport is False. ‘qiate’: QIATE, ‘qiate_se’: Standard error of QIATEs. None if exit_after_commonsupport

is False.

’qiate_diff’: QIATE minus QIATE at median. None if exit_after_commonsupport is False. ‘qiate_diff_se’: Standard error of QIATE minus QIATE at median. None if

exit_after_commonsupport is False.

’iate_data_df’: DataFrame with IATEs. None if exit_after_commonsupport is False. ‘iate_names_dic’: Dictionary containing names of IATEs. None if

exit_after_commonsupport is False.

’bala’: Effects of balancing tests. None if exit_after_commonsupport is False. ‘bala_se’: Standard error of effects of balancing tests. None if

exit_after_commonsupport is False.

’bala_effect_list’: Names of effects of balancing tests. None if

exit_after_commonsupport is False.

’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. ‘inputdata_on_support’: DataFrame of input data that on the common support. None if

exit_after_commonsupport is True.

Return type:

Dictionary.