conditional_means: Estimate mean rewards mu for each treatment a

In policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees

Estimate mean rewards \mu for each treatment a

Description

\mu_a = m(x) + (1-e_a(x))\tau_a(x)

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 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

# 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)

policytree documentation built on July 9, 2023, 6:30 p.m.