In Larsvanderlaan/npcausalML: Nonparametric estimation of the relative risk function

knitr::opts_chunk$set(echo = TRUE)

set.seed(123)
n <- 500
X <- runif(n, -1,1)
pi <- plogis(2*X)
A <- rbinom(n, 1, pi)
mu <- X + A * (1 + X)
mu1 <- 1 + 2*X
mu0 <- X
Y <- rnorm(n, mu, 0.5)

t <- Sys.time()
 plan(multiprocess,workers=1)

out <- EP_learn(list( Lrnr_xgboost$new(max_depth = 1, verbosity=0), Lrnr_xgboost$new(max_depth = 5, verbosity=0),Lrnr_xgboost$new(max_depth = 7),Lrnr_xgboost$new(max_depth = 3, verbosity=0), Lrnr_xgboost$new(max_depth = 10, verbosity=0)),
                V = data.frame(X=X), A = A, Y = Y, EY1W = mu1  , EY0W = mu0  , pA1W = pi, sieve_basis_generator_list = list(NULL, fourier_basis$new(1,1),fourier_basis$new(2,1) ) ,EP_learner_spec = EP_learner_spec_CATE, cross_validate = TRUE) 

 print(  Sys.time() - t)


out$cv_predictions

out$full_predictions

Larsvanderlaan/npcausalML documentation built on July 30, 2023, 4:32 p.m.