| predict.causal_forest {grf} | R Documentation |
Gets estimates of tau(x) using a trained causal forest.
## S3 method for class 'causal_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). |
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.
## 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)