Installation

Installing from CRAN.

install.packages("CRE")

Installing the latest developing version.

library(devtools)
install_github("NSAPH-Software/CRE", ref = "develop")

Import.

library("CRE")

Arguments

Data (required)

y The observed response/outcome vector (binary or continuos).

z The treatment/exposure/policy vector (binary).

X The covariate matrix (binary or continuos).

Parameters (not required)

method_parameters The list of parameters to define the models used, including:
- ratio_dis The ratio of data delegated to the discovery sub-sample (default: 0.5).
- ite_method_dis The method to estimate the individual treatment effect (ITE) on the discovery sub-sample (default: ‘aipw’) [1].
- ps_method_dis The estimation model for the propensity score on the discovery sub-sample (default: ‘SL.xgboost’).
- or_method_dis The estimation model for the outcome regressions estimate_ite_aipw on the discovery sub-sample (default: ‘SL.xgboost’).
- ite_method_inf The method to estimate the individual treatment effect (ITE) on the infernce sub-sample (default: ‘aipw’) [1].
- 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 fine tune 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 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).
- t_corr The threshold to define correlated rules (default: 1).
- t_pvalue The threshold to define statistically significant rules (default: 0.05).
- stability_selection Whether or not using stability selection for selecting the rules (default: TRUE).
- cutoff Threshold 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 for Regularization (i.e. 0: no penalty, 1: rules_length, 2: rules_length^2) (default: 1).

Additional Estimates (not required)

ite The estimated ITE vector. If given, both the ITE estimation steps in Discovery and Inference are skipped (default: NULL).

m_xgboost <- function(nthread = 12, ...) {
  SuperLearner::SL.xgboost(nthread = nthread, ...)
}

Examples

Example 1 (default parameters)

set.seed(9687)
dataset <- generate_cre_dataset(n = 1000, 
                                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"]]

cre_results <- cre(y, z, X)
summary(cre_results)
plot(cre_results)

Example 2 (personalized ite estimation)

set.seed(9687)
dataset <- generate_cre_dataset(n = 1000, 
                                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"]]

ite_pred <- ... # personalized ite estimation
cre_results <- cre(y, z, X, ite = ite_pred)
summary(cre_results)
plot(cre_results)

Example 3 (setting parameters)

set.seed(9687)
dataset <- generate_cre_dataset(n = 1000, 
                                  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 = c("x1","x2","x3","x4"),
                     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 = 0.8,
                     pfer = 0.1,
                     penalty_rl = 1)

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

More synthetic data sets can be generated using generate_cre_dataset().