grf (version 0.9.4)

predict.causal_forest: Predict with a causal forest

Description

Gets estimates of tau(x) using a trained causal forest.

Usage

# S3 method for causal_forest
predict(object, newdata = NULL, num.threads = NULL,
  estimate.variance = FALSE, ...)

Arguments

object

The trained forest.

newdata

Points at which predictions should be made. If NULL, makes out-of-bag predictions on the training set instead (i.e., provides predictions at Xi using only trees that did not use the i-th training example).

num.threads

Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.

estimate.variance

Whether variance estimates for hattau(x) are desired (for confidence intervals).

...

Additional arguments (currently ignored).

Value

Vector of predictions, along with (optional) variance estimates.

Examples

# NOT RUN {
# Train a causal forest.
n = 100; 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 = causal_forest(X, Y, W)

# Predict using the forest.
X.test = matrix(0, 101, p)
X.test[,1] = seq(-2, 2, length.out = 101)
c.pred = predict(c.forest, X.test)

# Predict on out-of-bag training samples.
c.pred = predict(c.forest)

# Predict with confidence intervals; growing more trees is now recommended.
c.forest = causal_forest(X, Y, W, num.trees = 500)
c.pred = predict(c.forest, X.test, estimate.variance = TRUE)

# }