predict.instrumental_forest function

Predict with an instrumental forest

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

## 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 prediction. If set to NULL, the software automatically selects an appropriate amount.
• estimate.variance: Whether variance estimates for $\hat\tau(x)$ are desired (for confidence intervals).
• ...: Additional arguments (currently ignored).

Returns

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)