#' Treatment Specific Mean
#'
#' Parameter definition for the Treatment Specific Mean (TSM): $E_W[E_{Y|A}(Y|A=a|W)|$. Currently supports multiple static intervention nodes.
#' Does yet not support dynamic rule or stochastic interventions.
#'
#' @section Current Issues:
#' \itemize{
#' \item clever covariates doesn't support updates; always uses initial (necessary for iterative TMLE, e.g. stochastic intervention)
#' \item doesn't integrate over possible counterfactuals (necessary for stochastic intervention)
#' \item clever covariate gets recalculated all the time (inefficient)
#' }
#' @importFrom R6 R6Class
#' @importFrom uuid UUIDgenerate
#' @importFrom methods is
#' @family Parameters
#' @keywords data
#'
#' @return \code{Param_base} object
#'
#' @format \code{\link{R6Class}} object.
#'
#' @section Constructor:
#' \code{define_param(Param_TSM, observed_likelihood, intervention_list, ..., outcome_node)}
#'
#' \describe{
#' \item{\code{observed_likelihood}}{A \code{\link{Likelihood}} corresponding to the observed likelihood
#' }
#' \item{\code{intervention_list}}{A list of objects inheriting from \code{\link{LF_base}}, representing the intervention.
#' }
#' \item{\code{...}}{Not currently used.
#' }
#' \item{\code{outcome_node}}{character, the name of the node that should be treated as the outcome
#' }
#' }
#'
#' @section Fields:
#' \describe{
#' \item{\code{cf_likelihood}}{the counterfactual likelihood for this treatment
#' }
#' \item{\code{intervention_list}}{A list of objects inheriting from \code{\link{LF_base}}, representing the intervention
#' }
#' }
#' @export
Param_TSM <- R6Class(
classname = "Param_TSM",
portable = TRUE,
class = TRUE,
inherit = Param_base,
public = list(
initialize = function(observed_likelihood, intervention_list, ..., outcome_node = "Y") {
super$initialize(observed_likelihood, ..., outcome_node = outcome_node)
if (!is.null(observed_likelihood$censoring_nodes[[outcome_node]])) {
# add delta_Y=0 to intervention list
outcome_censoring_node <- observed_likelihood$censoring_nodes[[outcome_node]]
censoring_intervention <- define_lf(LF_static, outcome_censoring_node, value = 1)
intervention_list <- c(intervention_list, censoring_intervention)
}
private$.cf_likelihood <- make_CF_Likelihood(observed_likelihood, intervention_list)
},
clever_covariates = function(tmle_task = NULL, fold_number = "full") {
if (is.null(tmle_task)) {
tmle_task <- self$observed_likelihood$training_task
}
intervention_nodes <- names(self$intervention_list)
pA <- self$observed_likelihood$get_likelihoods(tmle_task, intervention_nodes, fold_number)
cf_pA <- self$cf_likelihood$get_likelihoods(tmle_task, intervention_nodes, fold_number)
HA <- cf_pA / pA
# collapse across multiple intervention nodes
if (!is.null(ncol(HA)) && ncol(HA) > 1) {
HA <- apply(HA, 1, prod)
}
HA <- bound(HA, c(-40, 40))
return(list(Y = unlist(HA, use.names = FALSE)))
},
estimates = function(tmle_task = NULL, fold_number = "full") {
if (is.null(tmle_task)) {
tmle_task <- self$observed_likelihood$training_task
}
# todo: extend for stochastic
cf_task <- self$cf_likelihood$enumerate_cf_tasks(tmle_task)[[1]]
# cf_task <- self$cf_likelihood$cf_tasks[[1]]
Y <- tmle_task$get_tmle_node(self$outcome_node, impute_censoring = TRUE)
# clever_covariates happen here (for this param) only, but this is repeated computation
HA <- self$clever_covariates(tmle_task, fold_number)[[self$outcome_node]]
# clever_covariates happen here (for all fit params), and this is repeated computation
EYA <- unlist(self$observed_likelihood$get_likelihood(tmle_task, self$outcome_node, fold_number), use.names = FALSE)
# clever_covariates happen here (for all fit params), and this is repeated computation
EY1 <- unlist(self$cf_likelihood$get_likelihood(cf_task, self$outcome_node, fold_number), use.names = FALSE)
# todo: separate out psi
# todo: make this a function of f(W)
psi <- mean(EY1)
IC <- HA * (Y - EYA) + EY1 - psi
result <- list(psi = psi, IC = IC)
return(result)
}
),
active = list(
name = function() {
param_form <- sprintf("E[%s_{%s}]", self$outcome_node, self$cf_likelihood$name)
return(param_form)
},
cf_likelihood = function() {
return(private$.cf_likelihood)
},
intervention_list = function() {
return(self$cf_likelihood$intervention_list)
},
update_nodes = function() {
return(self$outcome_node)
}
),
private = list(
.type = "TSM",
.cf_likelihood = NULL,
.supports_outcome_censoring = TRUE
)
)
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