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

This function estimates the Grouped Average Treatment Effects (GATEs) under cross-validation where the groups are determined by a continuous score. The details of the methods for this design are given in Imai and Li (2022).

Usage

GATEcv(T, tau, Y, ind, ngates = 5)

Arguments

T

A vector of the unit-level binary treatment receipt variable for each sample.

tau

A matrix where the ith column is the unit-level continuous score for treatment assignment generated in the ith fold. Conditional Average Treatment Effect is one possible measure.

Y

A vector of the outcome variable of interest for each sample.

ind

A vector of integers (between 1 and number of folds inclusive) indicating which testing set does each sample belong to.

ngates

The number of groups to separate the data into. The groups are determined by tau. Default is 5.

Value

A list that contains the following items:

gate

The estimated vector of GATEs under cross-validation of length ngates arranged in order of increasing tau.

sd

The estimated vector of standard deviation of GATEs under cross-validation.

References

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

Author

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

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$gate
#> [1]  1.0 -4.5
gatelist$sd
#> [1] 4.881940 3.452053