RcausalEGM (version 0.3.3)

get_est: Make predictions with causalEGM model.

Description

When x is NULL, the conditional average treatment effect (CATE), namely tau(v), is estimated using a trained causalEGM model. When x is provided, estimating the potential outcome y given treatment x and covariates v using a trained causalEGM model.

Usage

get_est(object, v, x = NULL)

Value

Vector of predictions.

Arguments

object

An object of class "causalegm".

v

is the covariates, two-dimensional array with size n by p.

x

is the optional treatment variable, one-dimensional array with size n. Defaults to NULL.

Examples

# \donttest{
#Generate a simple simulation data.
n <- 1000
p <- 10
v <- matrix(rnorm(n * p), n, p)
x <- rbinom(n, 1, 0.4 + 0.2 * (v[, 1] > 0))
y <- pmax(v[, 1], 0) * x + v[, 2] + pmin(v[, 3], 0) + rnorm(n)
model <- causalegm(x=x, y=y, v=v, n_iter=3000)
n_test <- 100
v_test <- matrix(rnorm(n_test * p), n_test, p)
x_test <- rbinom(n_test, 1, 0.4 + 0.2 * (v_test[, 1] > 0))
pred_cate <- get_est(model, v = v_test) # CATE estimate
pred_y <- get_est(model, v = v_test, x = x_test) # y given treatment x plus covariates v
# }