R: Predict with a causal forest

predict.causal_forest {grf} | R Documentation

## Predict with a causal forest

### Description

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

### Usage

    ## S3 method for class 'causal_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).
`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

    ## Not run:
    # Train a causal forest.
    n = 100; p = 10
    X = matrix(rnorm(n*p), n, p)
    W = rbinom(n, 1, 0.5)
    Y = pmax(X[,1], 0) * W + X[,2] + pmin(X[,3], 0) + rnorm(n)
    c.forest = causal_forest(X, Y, W)

    # Predict using the forest.
    X.test = matrix(0, 101, p)
    X.test[,1] = seq(-2, 2, length.out = 101)
    c.pred = predict(c.forest, X.test)

    # Predict on out-of-bag training samples.
    c.pred = predict(c.forest)

    # Predict with confidence intervals; growing more trees is now recommended.
    c.forest = causal_forest(X, Y, W, num.trees = 500)
    c.pred = predict(c.forest, X.test, estimate.variance = TRUE)

    ## End(Not run)

[Package grf version 0.10.2 Index]