R/causal_forest-scores.R
, R/causal_survival_forest-scores.R
, R/instrumental_forest-scores.R
, and 2 more
conditional_means.Rd
\(\mu_a = m(x) + (1-e_a(x))\tau_a(x)\)
# 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, ...)
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
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 \(a\)
# \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)#> A B C #> [1,] -0.6994923 -1.4564177 -1.0257161 #> [2,] 0.6248907 1.2550443 0.3424060 #> [3,] -1.4958288 -1.1039313 -0.8028334 #> [4,] 0.5499072 0.8463664 -0.3342277 #> [5,] 1.2232335 0.4135211 0.5246127 #> [6,] 1.8284059 1.9465789 1.4300934