grf (version 1.0.0)

best_linear_projection: Estimate the best linear projection of a conditional average treatment effect using a causal forest.

Description

Let tau(Xi) = E[Y(1) - Y(0) | X = Xi] be the CATE, and Ai be a vector of user-provided covariates. This function provides a (doubly robust) fit to the linear model

Usage

best_linear_projection(forest, A = NULL, subset = NULL)

Arguments

forest

The trained forest.

A

The covariates we want to project the CATE onto.

subset

Specifies subset of the training examples over which we estimate the ATE. WARNING: For valid statistical performance, the subset should be defined only using features Xi, not using the treatment Wi or the outcome Yi.

Value

An estimate of the best linear projection, along with coefficient standard errors.

Details

tau(Xi) ~ beta_0 + Ai * beta

Procedurally, we do so be regressing doubly robust scores derived from the causal forest against the Ai. Note the covariates Ai may consist of a subset of the Xi, or they may be distince The case of the null model tau(Xi) ~ beta_0 is equivalent to fitting an average treatment effect via AIPW.

References

Chernozhukov, Victor, and Vira Semenova. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions." arXiv preprint arXiv:1702.06240 (2017).

Examples

# NOT RUN {
n <- 800
p <- 5
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.25 + 0.5 * (X[, 1] > 0))
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
forest <- causal_forest(X, Y, W)
best_linear_projection(forest, X[,1:2])
# }
# NOT RUN {
# }