# Generate some data
n <- 100
b0 <- 1
b1 <- 1.5
b2 <- 2
W1 <- runif(n, min = 1, max = 10)
exposure_prob <- .5
dat_treat <- glm_data(
Y ~ b0+b1*log(W1)+b2*A,
W1 = W1,
A = rbinom(n, 1, exposure_prob)
)
dat_notreat <- glm_data(
Y ~ b0+b1*log(W1),
W1 = W1
)
learners <- list(
mars = list(
model = parsnip::set_engine(
parsnip::mars(
mode = "regression", prod_degree = 3
),
"earth"
)
)
)
ate <- rctglm_with_prognosticscore(
formula = Y ~ .,
exposure_indicator = A,
exposure_prob = exposure_prob,
data = dat_treat,
family = gaussian(),
estimand_fun = "ate",
data_hist = dat_notreat,
learners = learners)
prog(ate)
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