grf (version 0.9.4)

estimate_average_effect: Estimate average treatment effects using a causal forest

Description

Gets estimates of one of the following

Usage

estimate_average_effect(forest, target.sample = c("all", "treated",
  "control"), method = c("AIPW", "TMLE"))

Arguments

forest

The trained forest.

target.sample

Which sample to aggregate treatment effects over.

method

Method used for doubly robust inference. Can be either augmented inverse-propensity weighting (AIPW), or targeted maximum likelihood estimation (TMLE).

Value

Vector of predictions, along with (optional) variance estimates.

Details

The (conditional) average treatment effect (target.sample = all): sum_i = 1^n E[Y(1) - Y(0) | X = Xi] / n The (conditional) average treatment effect on the treated (target.sample = treated): sum_Wi = 1 E[Y(1) - Y(0) | X = Xi] / |i : Wi = 1| The (conditional) average treatment effect on the controls (target.sample = control): sum_Wi = 0 E[Y(1) - Y(0) | X = Xi] / |i : Wi = 0|

Examples

# NOT RUN {
# Train a causal forest.
n = 50; 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)
# Estimate the conditional average treatment effect on the full sample (CATE).
estimate_average_effect(c.forest, target.sample = "all")

# Estimate the conditional average treatment effect on the treated sample (CATT).
# We don't expect much difference between the CATE and the CATT in this example,
# since treatment assignment was randomized.
estimate_average_effect(c.forest, target.sample = "treated")

# }