GATEcv: Estimation of the Grouped Average Treatment Effects (GATEs)...

In evalHTE: Evaluating Heterogeneous Treatment Effects

Estimation of the Grouped Average Treatment Effects (GATEs) in Randomized Experiments Under Cross Validation

Description

Estimates grouped average treatment effects from cross-validated scores.

Usage

GATEcv(D, tau, Y, ind, ngates = 5, centered = FALSE)

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 GATEcv for inference.

Value

A list with group estimates gate and standard errors sd, ordered by increasing score.

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))
gatelist <- GATEcv(D, tau, Y, ind, ngates = 2)
gatelist$gate
gatelist$sd

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