predict.instrumental_forest: Predict with an instrumental forest

In grf: Generalized Random Forests

Predict with an instrumental forest

Description

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

Usage

## S3 method for class 'instrumental_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). Note that this matrix should have the number of columns as the training matrix, and that the columns must appear in the same order.
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

# Train an instrumental forest.
n <- 2000
p <- 5
X <- matrix(rbinom(n * p, 1, 0.5), n, p)
Z <- rbinom(n, 1, 0.5)
Q <- rbinom(n, 1, 0.5)
W <- Q * Z
tau <-  X[, 1] / 2
Y <- rowSums(X[, 1:3]) + tau * W + Q + rnorm(n)
iv.forest <- instrumental_forest(X, Y, W, Z)

# Predict on out-of-bag training samples.
iv.pred <- predict(iv.forest)

# Estimate a (local) average treatment effect.
average_treatment_effect(iv.forest)

grf documentation built on Oct. 1, 2023, 1:07 a.m.