policytree (version 1.2.3)

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 causal_survival_forest
conditional_means(object, ...)
# S3 method for instrumental_forest
conditional_means(object, ...)
# S3 method for multi_arm_causal_forest
conditional_means(object, outcome = 1, ...)
conditional_means(object, ...)

Value

A matrix of estimated mean rewards

Arguments

object

An appropriate causal forest type object

...

Additional arguments

outcome

Only used with multi arm causal forets. In the event the forest is trained with multiple outcomes Y, a column number/name specifying the outcome of interest. Default is 1.

Methods (by class)

• conditional_means(causal_forest): Mean rewards \(\mu\) for control/treated

• conditional_means(causal_survival_forest): Mean rewards \(\mu\) for control/treated

• conditional_means(instrumental_forest): Mean rewards \(\mu\) for control/treated

• conditional_means(multi_arm_causal_forest): Mean rewards \(\mu\) for each treatment \(a\)

Examples

# \donttest{
# Compute conditional means for a multi-arm causal forest
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- as.factor(sample(c("A", "B", "C"), n, replace = TRUE))
Y <- X[, 1] + X[, 2] * (W == "B") + X[, 3] * (W == "C") + runif(n)
forest <- grf::multi_arm_causal_forest(X, Y, W)
mu.hats <- conditional_means(forest)
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)
# }