hetcv.test: The Heterogeneity Test for Grouped Average Treatment Effects...

In evalHTE: Evaluating Heterogeneous Treatment Effects

The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) under Cross Validation in Randomized Experiments

Description

Tests whether treatment effects are equal across cross-validated score groups.

Usage

hetcv.test(D, tau, Y, ind, ngates = 5, centered = TRUE)

Arguments

D	Binary treatment indicator (0 or 1).
tau	A matrix of scores with one column per fold. Column i contains predictions for all observations from a model trained without fold i.
Y	Outcome vector.
ind	Integer validation-fold labels starting at 1.
ngates	Number of groups (at least 2).
centered	Whether to center outcomes before estimation.

Details

Uses hetcv.test for inference.

Value

A list with test statistic stat and p-value pval.

Author(s)

Michael Lingzhi Li, Technology and Operations Management, Harvard Business School [e-mail], https://www.michaellz.com/;

References

Imai and Li (2022). “Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments”,

Examples

D = c(1,0,1,0,1,0,1,0)
tau = matrix(c(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,-0.5,-0.3,-0.1,0.1,0.3,0.5,0.7,0.9),nrow = 8, ncol = 2)
Y = c(4,5,0,2,4,1,-4,3)
ind = c(rep(1,4),rep(2,4))
hettestlist <- hetcv.test(D,tau,Y,ind,ngates=2)
hettestlist$stat
hettestlist$pval

evalHTE documentation built on Sept. 7, 2026, 9:06 a.m.