Predict CATEs at new covariate values from a fitted causal forest

Predict CATEs at new covariate values from a fitted causal forest

Usage

morie_causal_forest_predict(forest, newx)

Arguments

forest

The forest element of a morie_causal_forest() fit.

newx

Covariate matrix with the same columns as the training X.

Value

Numeric vector of predicted conditional treatment effects.

References

Wager S, Athey S (2018). Estimation and inference of heterogeneous treatment effects using random forests. JASA 113(523), 1228-1242.

Examples

set.seed(1)
n <- 120
X <- matrix(rnorm(n * 2), n, 2)
d <- rbinom(n, 1, 0.5)
y <- 1 + X[, 1] + d * (0.5 + X[, 2]) + rnorm(n, 0, 0.3)
fit <- morie_causal_forest(y, d, X, n_trees = 30L, min_leaf = 10L)
morie_causal_forest_predict(fit$forest, X[1:5, , drop = FALSE])
#> [1] 0.1444736 0.1444736 0.1444736 0.1444736 0.1444736