context("ATT interventions: treatment effect amongst the treated")
library(sl3)
library(tmle3)
library(uuid)
library(assertthat)
library(data.table)
library(future)
# setup data for test
data(cpp)
data <- as.data.table(cpp)
data$parity01 <- as.numeric(data$parity > 0)
data$parity01_fac <- factor(data$parity01)
data$haz01 <- as.numeric(data$haz > 0)
data[is.na(data)] <- 0
node_list <- list(
W = c("sexn"),
A = "parity01",
Y = "haz01"
)
qlib <- make_learner_stack(
"Lrnr_mean",
"Lrnr_glm_fast"
)
glib <- make_learner_stack(
"Lrnr_mean",
"Lrnr_glm_fast"
)
logit_metalearner <- make_learner(
Lrnr_solnp, metalearner_logistic_binomial,
loss_loglik_binomial
)
Q_learner <- make_learner(Lrnr_sl, qlib, logit_metalearner)
g_learner <- make_learner(Lrnr_sl, glib, logit_metalearner)
learner_list <- list(Y = Q_learner, A = g_learner)
tmle_spec <- tmle_ATT(1, 0)
# define data
tmle_task <- tmle_spec$make_tmle_task(data, node_list)
# LF_fit$undebug("get_likelihood")
# estimate likelihood
initial_likelihood <- tmle_spec$make_initial_likelihood(tmle_task, learner_list)
updater <- tmle3_Update$new(
cvtmle = FALSE, convergence_type = "sample_size",
constrain_step = TRUE, one_dimensional = TRUE, delta_epsilon = 0.001,
optim_delta_epsilon = FALSE
)
# debugonce(updater$update_step)
targeted_likelihood <- Targeted_Likelihood$new(initial_likelihood, updater)
# define parameter
tmle_params <- tmle_spec$make_params(tmle_task, targeted_likelihood)
updater$tmle_params <- tmle_params
att <- tmle_params[[1]]
# fit tmle update
tmle_fit <- fit_tmle3(
tmle_task, targeted_likelihood, list(att), updater,
max_it
)
# extract results
tmle3_psi <- tmle_fit$summary$tmle_est
tmle3_se <- tmle_fit$summary$se
tmle3_epsilon <- updater$epsilons[[1]]$Y
#################################################
# compare with the tmle package
library(tmle)
# construct likelihood estimates
# tasks for A=1 and A=0
cf_task1 <- att$cf_likelihood_treatment$cf_tasks[[1]]
cf_task0 <- att$cf_likelihood_control$cf_tasks[[1]]
# get Q
EY1 <- initial_likelihood$get_likelihoods(cf_task1, "Y")
EY0 <- initial_likelihood$get_likelihoods(cf_task0, "Y")
EY1_final <- targeted_likelihood$get_likelihoods(cf_task1, "Y")
EY0_final <- targeted_likelihood$get_likelihoods(cf_task0, "Y")
# EY0 <- rep(0, length(EY1)) # not used
Q <- cbind(EY0, EY1)
# get G
pA1 <- initial_likelihood$get_likelihoods(cf_task1, "A")
# debugonce(oneStepATT)
tmle_classic_fit <- tmle(
Y = tmle_task$get_tmle_node("Y"),
A = tmle_task$get_tmle_node("A"),
W = cbind(tmle_task$get_tmle_node("W"), tmle_task$get_tmle_node("W")),
Q = Q,
g1W = pA1,
family = "binomial",
alpha = 0.995,
target.gwt = FALSE
)
# extract estimates
classic_psi <- tmle_classic_fit$estimates$ATT$psi
classic_se <- sqrt(tmle_classic_fit$estimates$ATT$var.psi)
tol <- 1 / sqrt(tmle_task$nrow)
test_that("psi matches result from classic package", {
expect_equal(tmle3_psi, classic_psi, tol)
})
test_that("se matches result from classic package", {
expect_equal(tmle3_se, classic_se, tol)
})
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