conditional_means.causal_forest {policytree} | R Documentation |
Estimate mean rewards
for each treatment
Description
Usage
## S3 method for class 'causal_forest'
conditional_means(object, ...)
## S3 method for class 'causal_survival_forest'
conditional_means(object, ...)
## S3 method for class 'instrumental_forest'
conditional_means(object, ...)
## S3 method for class 'multi_arm_causal_forest'
conditional_means(object, outcome = 1, ...)
conditional_means(object, ...)
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. |
Value
A matrix of estimated mean rewards
Methods (by class)
-
conditional_means(causal_forest)
: Mean rewardsfor control/treated
-
conditional_means(causal_survival_forest)
: Mean rewardsfor control/treated
-
conditional_means(instrumental_forest)
: Mean rewardsfor control/treated
-
conditional_means(multi_arm_causal_forest)
: Mean rewardsfor each treatment
Examples
# 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)
[Package policytree version 1.2.3 Index]