R: Causal rule ensemble

cre {CRE}

R Documentation

Causal rule ensemble

Description

Performs the Causal Rule Ensemble on a data set with a response variable, a treatment variable, and various features.

Usage

cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)

Arguments

`y`	An observed response vector.
`z`	A treatment vector.
`X`	A covariate matrix (or a data frame).
`method_params`	The list of parameters to define the models used, including: Parameters for Honest Splitting ratio_dis: The ratio of data delegated to rules discovery (default: 0.5). Parameters for Discovery ite_method_dis: The method to estimate the discovery sample ITE (default: 'aipw'). ps_method_dis: The estimation model for the propensity score on the discovery subsample (default: 'SL.xgboost'). or_method_dis: The estimation model for the outcome regressions estimate_ite_aipw on the discovery subsample (default: 'SL.xgboost'). Parameters for Inference ite_method_inf: The method to estimate the inference sample ITE (default: 'aipw'). ps_method_inf: The estimation model for the propensity score on the inference subsample (default: 'SL.xgboost'). or_method_inf: The estimation model for the outcome regressions in estimate_ite_aipw on the inference subsample (default: 'SL.xgboost').
`hyper_params`	The list of hyper parameters to finetune the method, including: intervention_vars: Intervention-able variables used for Rules Generation (default: NULL). offset: Name of the covariate to use as offset (i.e. 'x1') for T-Poisson ITE Estimation. NULL if offset is not used (default: NULL). ntrees_rf: A number of decision trees for random forest (default: 20). ntrees_gbm: A number of decision trees for the generalized boosted regression modeling algorithm. (default: 20). node_size: Minimum size of the trees' terminal nodes (default: 20). max_nodes: Maximum number of terminal nodes per tree (default: 5). max_depth: Maximum rules length (default: 3). replace: Boolean variable for replacement in bootstrapping for rules generation by random forest (default: TRUE). t_decay: The decay threshold for rules pruning (default: 0.025). t_ext: The threshold to define too generic or too specific (extreme) rules (default: 0.01, range: (0,0.5)). t_corr: The threshold to define correlated rules (default: 1, range: (0,+inf)). t_pvalue: the threshold to define statistically significant rules (default: 0.05, range: (0,1)). stability_selection: Whether or not using stability selection for selecting the rules (default: TRUE). cutoff: Threshold (percentage) defining the minimum cutoff value for the stability scores (default: 0.9). pfer: Upper bound for the per-family error rate (tolerated amount of falsely selected rules) (default: 1). penalty_rl: Order of penalty for rules length during LASSO regularization (i.e. 0: no penalty, 1: rules_length, 2: rules_length^2) (default: 1).
`ite`	The estimated ITE vector. If given both the ITE estimation steps in Discovery and Inference are skipped (default: NULL).

Value

An S3 object containing:

A number of Decision Rules extracted at each step (M).
A data.frame of Conditional Average Treatment Effect decomposition estimates with corresponding uncertainty quantification (CATE).
A list of Method Parameters (method_params).
A list of Hyper Parameters (hyper_params).
An Individual Treatment Effect predicted (ite_pred).

Examples



set.seed(2021)
dataset <- generate_cre_dataset(n = 400, rho = 0, n_rules = 2, p = 10,
                                effect_size = 2, binary_covariates = TRUE,
                                binary_outcome = FALSE, confounding = "no")
y <- dataset[["y"]]
z <- dataset[["z"]]
X <- dataset[["X"]]

method_params <- list(ratio_dis = 0.25,
                      ite_method_dis="aipw",
                      ps_method_dis = "SL.xgboost",
                      oreg_method_dis = "SL.xgboost",
                      ite_method_inf = "aipw",
                      ps_method_inf = "SL.xgboost",
                      oreg_method_inf = "SL.xgboost")

hyper_params <- list(intervention_vars = NULL,
                     offset = NULL,
                     ntrees_rf = 20,
                     ntrees_gbm = 20,
                     node_size = 20,
                     max_nodes = 5,
                     max_depth = 3,
                     t_decay = 0.025,
                     t_ext = 0.025,
                     t_corr = 1,
                     t_pvalue = 0.05,
                     replace = FALSE,
                     stability_selection = TRUE,
                     cutoff = 0.6,
                     pfer = 0.1,
                     penalty_rl = 1)

cre_results <- cre(y, z, X, method_params, hyper_params)

[Package CRE version 0.2.0 Index]