evalITR (version 1.1.0)

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

Value

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

Arguments

T

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.

Author

Michael Lingzhi Li, Technology and Operations Management, Harvard Business School , https://www.michaellz.com/;

Details

Inference assumes a fixed number of folds and stable scoring rules. Each fold needs at least two observations per treatment arm. See GATE for grouping and covariance details.

References

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

Examples

T = 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(T, tau, Y, ind, ngates = 2)
gatelist$gate
gatelist$sd