View source: R/instrumental_forest.R
predict.instrumental_forest | 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 |
... |
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)
# Estimate a (local) average treatment effect.
average_treatment_effect(iv.forest)
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