grf (version 0.9.6)

average_treatment_effect: Estimate average treatment effects using a causal forest

Description

Gets estimates of one of the following.

• 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|

• The overlap-weighted (conditional) average treatment effect sum_i = 1^n e(Xi) (1 - e(Xi)) E[Y(1) - Y(0) | X = Xi] / sum_i = 1^n e(Xi) (1 - e(Xi)), where e(x) = P[Wi = 1 | Xi = x].

This last estimand is recommended by Li, Morgan, and Zaslavsky (JASA, 2017) in case of poor overlap (i.e., when the propensities e(x) may be very close to 0 or 1), as it doesn't involve dividing by estimated propensities.

Usage

average_treatment_effect(forest, target.sample = c("all", "treated",
  "control", "overlap"), 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

An estimate of the average treatment effect, along with standard error.

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).
average_treatment_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.
average_treatment_effect(c.forest, target.sample = "treated")
# }
# NOT RUN {
# }