| predict.instrumental_forest {grf} | R Documentation |
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, ... )
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). |
Vector of predictions, along with (optional) variance estimates.
# 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)