sigmaATE_update <- function(tmledata, Q.trunc = 0.001, ...) {
# fix points where Q is already 0 or 1 - perfect prediction
subset <- with(tmledata, which(0 < Qk & Qk < 1))
eps_q <- 0
tmledata$Qktrunc <- with(tmledata, truncate(Qk, Q.trunc))
if (length(subset) > 0) {
# fluctuate Q
qfluc <- logit_fluctuate(tmledata, Y ~ -1 + HA + offset(qlogis(Qktrunc)))
eps_q <- qfluc$eps
tmledata$Qk <- with(tmledata, plogis(qlogis(Qktrunc) + HA * eps_q))
tmledata$Q1k <- with(tmledata, plogis(qlogis(Q1k) + H1 * eps_q))
tmledata$Q0k <- with(tmledata, plogis(qlogis(Q0k) + H0 * eps_q))
}
list(tmledata = tmledata, coef = eps_q)
}
sigmaATE_estimate <- function(tmledata, Q.trunc = 0.001, ...) {
ATE <- mean(with(tmledata, Q1k - Q0k))
tmledata$HA <- with(tmledata, 2 * (Q1k - Q0k - ATE) * (A/gk - (1 - A)/(1 - gk)))
tmledata$H1 <- with(tmledata, 2 * (Q1k - Q0k - ATE)/gk)
tmledata$H0 <- with(tmledata, -2 * (Q1k - Q0k - ATE)/(1 - gk))
# influence curve
sigma <- var(with(tmledata, Q1k - Q0k))
Dstar_sigma <- with(tmledata, (Q1k - Q0k - ATE)^2 - sigma + HA * (Y - Qk))
list(tmledata = tmledata, ests = c(sigma = sigma), Dstar = list(Dstar_sigma = Dstar_sigma))
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.