This provides a basic recipe for HTE estimation that can be extended by providing additional information about models to be estimated and what quantities of interest should be returned based on those models. This basic model includes only linear models for nuisance function estimation, and basic diagnostics.
basic_config()HTE_cfg object
Additional models, diagnostics and quantities of interest should be added using their respective helper functions provided as part of the Recipe API.
To see an example analysis, read vignette("experimental_analysis") in the context
of an experiment, vignette("experimental_analysis") for an observational study, or
vignette("methodological_details") for a deeper dive under the hood.
library("dplyr")
basic_config() %>%
add_known_propensity_score("ps") %>%
add_outcome_model("SL.glm.interaction") %>%
add_outcome_model("SL.glmnet", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
add_outcome_model("SL.glmnet.interaction", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
add_outcome_diagnostic("RROC") %>%
add_effect_model("SL.glm.interaction") %>%
add_effect_model("SL.glmnet", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
add_effect_model("SL.glmnet.interaction", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
add_effect_diagnostic("RROC") %>%
add_moderator("Stratified", x2, x3) %>%
add_moderator("KernelSmooth", x1, x4, x5) %>%
add_vimp(sample_splitting = FALSE) -> hte_cfg