Subgroup CATE estimation.

This function estimates the conditional average treatment effect for each subgroup given by the fitted decision tree. The conditional average treatment effect is estimated as the difference in the average outcome between treated and control units that fall within each subgroup (i.e., each leaf node in the decision tree).

Usage

estimate_group_cates(fit, X, Y, Z)

Arguments

fit

Fitted subgroup model used to determine subgroup membership of individuals. Typically, this is a party or rpart object, but any model object that can be used to determine subgroup membership via predict(fit, x, type = 'node') can be used. If predict(fit, x, type = 'node') returns an error, then subgroups are determined based upon the unique values of predict(fit, x).

X

A tibble, data.frame, or matrix of covariates.

Y

A vector of outcomes.

Z

A vector of treatments.

Value

Estimated subgroup average treatment effects tibble with the following columns:

leaf_id

Leaf node identifier.

subgroup

String representation of the subgroup.

estimate

Estimated conditional average treatment effect for the subgroup.

variance

Asymptotic variance of the estimated conditional average treatment effect.

.var1

Sample variance for treated observations in the subgroup.

.var0

Sample variance for control observations in the subgroup.

.n1

Number of treated observations in the subgroup.

.n0

Number of control observations in the subgroup.

.sample_idxs

Indices of (holdout) observations in the subgroup.

Examples

n <- 100
p <- 5
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
Z <- rbinom(n, 1, 0.5)
Y <- 2 * Z * (X[, 1] > 0) + X[, 2] + rnorm(n, 0.1)

# causal distillation tree output
out <- causalDT(X, Y, Z)
# compute subgroup CATEs manually
group_cates <- estimate_group_cates(
  out$student_fit$fit,
  X = X[out$holdout_idxs, , drop = FALSE],
  Y = Y[out$holdout_idxs],
  Z = Z[out$holdout_idxs]
)
all.equal(out$estimate, group_cates)
#> [1] TRUE