R: Predict with an instrumental forest

predict.instrumental_forest {grf} | R Documentation

## 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)

[Package grf version 2.1.0 Index]