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 continuous).

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

X The covariate matrix (binary or continuous).

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 The method to estimate the individual treatment effect (default: “aipw”) [1].
- learner_ps The (SuperLearner) model for the propensity score estimation (default: “SL.xgboost”, used only for “aipw”,“bart”,“cf” ITE estimators).
- learner_y The (SuperLearner) model for the outcome estimation (default: “SL.xgboost”, used only for “aipw”,“slearner”,“tlearner” and “xlearner” ITE estimators).

hyper_params The list of hyper parameters to fine tune the method, including:
- intervention_vars Intervention-able variables used for Rules Generation (default: NULL).
- ntrees The number of decision trees for random forest (default: 20).
- node_size Minimum size of the trees’ terminal nodes (default: 20).
- max_rules Maximum number of candidate decision rules (default: 50).
- max_depth Maximum rules length (default: 3).
- 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).
- stability_selection Method for stability selection for selecting the rules. vanilla for stability selection, error_control for stability selection with error control and no for no stability selection (default: vanilla).
- B Number of bootstrap samples for stability selection in rules selection and uncertainty quantification in estimation (default: 20). - subsample Bootstrap ratio subsample for stability selection in rules selection and uncertainty quantification in estimation (default: 0.5). - offset Name of the covariate to use as offset (i.e. “x1”) for T-Poisson ITE Estimation. NULL if not used (default: NULL).
- cutoff Threshold defining the minimum cutoff value for the stability scores in Stability Selection (default: 0.9).
- pfer Upper bound for the per-family error rate (tolerated amount of falsely selected rules) in Error Control Stability Selection (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)
ite_pred <- predict(cre_results, X)

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)
ite_pred <- predict(cre_results, X)

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 = 0.5,
                      ite_method ="aipw",
                      learner_ps = "SL.xgboost",
                      learner_y = "SL.xgboost")

hyper_params <- list(intervention_vars = c("x1","x2","x3","x4"),
                     offset = NULL,
                     ntrees = 20,
                     node_size = 20,
                     max_rules = 50,
                     max_depth = 3,
                     t_decay = 0.025,
                     t_ext = 0.025,
                     t_corr = 1,
                     stability_selection = "vanilla",
                     cutoff = 0.8,
                     pfer = 1,
                     B = 10,
                     subsample = 0.5)

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

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