policytree (version 1.0)

conditional_means.causal_forest: Estimate mean rewards $\mu$ for each treatment $a$

Description

${\mu }_a=m(x)+(1-e_a(x)){\tau }_a(x)$

Usage

# S3 method for causal_forest
conditional_means(object, ...)
# S3 method for instrumental_forest
conditional_means(object, ...)
# S3 method for multi_causal_forest
conditional_means(object, ...)
conditional_means(object, ...)

Arguments

object

An appropriate causal forest type object

...

Additional arguments

Value

A matrix of estimated mean rewards

Methods (by class)

• causal_forest: Mean rewards $\mu$ for control/treated

• instrumental_forest: Mean rewards $\mu$ for control/treated

• multi_causal_forest: Mean rewards $\mu$ for each treatment $a$

Examples

# NOT RUN {
# Compute conditional means for a multi_causal forest
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- sample(c("A", "B", "C"), n, replace = TRUE)
Y <- X[, 1] + X[, 2] * (W == "B") + X[, 3] * (W == "C") + runif(n)
forests <- multi_causal_forest(X = X, Y = Y, W = W)
mu.hats <- conditional_means(forests)
head(mu.hats)

# Compute conditional means for a causal forest
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
c.forest <- grf::causal_forest(X, Y, W)
mu.hats <- conditional_means(c.forest)
# }