Description Usage Arguments Value Methods (by class) Examples
μ_a = m(x) + (1e_a(x))τ_a(x)
1 2 3 4 5 6 7 8 9 10  ## S3 method for class 'causal_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, ...)

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. 
A matrix of estimated mean rewards
causal_forest
: Mean rewards μ for control/treated
instrumental_forest
: Mean rewards μ for control/treated
multi_arm_causal_forest
: Mean rewards μ for each treatment a
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # Compute conditional means for a multiarm 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)

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