Predict with an M-learner with random (survvial) forest — predict.surv_fl

Obtain estimated tau(X) using a trained M-learner with random (survvial) forest model

# S3 method for surv_fl_grf
predict(object, newdata = NULL, ...)

Arguments

object: An surv_fl_grf object
newdata: Covariate matrix to make predictions on. If null, return the tau(X) predictions on the training data
...: Additional arguments (currently not used)

Value

A vector of estimated conditional average treatment effects

Details

Remark: CATE predictions can only be made at the time point used to define the outcome in the trained model

Examples

# \donttest{
n <- 1000; p <- 25
t0 <- 0.2
Y.max <- 2
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
numeratorT <- -log(runif(n))
T <- (numeratorT / exp(1 * X[ ,1, drop = FALSE] + (-0.5 - 1 * X[ ,2, drop = FALSE]) * W)) ^ 2
failure.time <- pmin(T, Y.max)
numeratorC <- -log(runif(n))
censor.time <- (numeratorC / (4 ^ 2)) ^ (1 / 2)
Y <- pmin(failure.time, censor.time)
D <- as.integer(failure.time <= censor.time)
n.test <- 500
X.test <- matrix(rnorm(n.test * p), n.test, p)

surv.fl.grf.fit <- surv_fl_grf(X, Y, W, D, t0, W.hat = 0.5)
cate <- predict(surv.fl.grf.fit)
cate.test <- predict(surv.fl.grf.fit, X.test)
# }