Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in Randomized Experiments Under Cross Validation

This function estimates AUPEC. The details of the methods for this design are given in Imai and Li (2019).

Usage

AUPECcv(T, tau, Y, ind, centered = TRUE)

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.

Y

The outcome variable of interest.

ind

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

centered

If TRUE, the outcome variables would be centered before processing. This minimizes the variance of the estimator. Default is TRUE.

Value

A list that contains the following items:

aupec

The estimated AUPEC.

sd

The estimated standard deviation of AUPEC.

References

Imai and Li (2019). “Experimental Evaluation of Individualized Treatment Rules”,

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))
aupeclist <- AUPECcv(T, tau, Y, ind)
aupeclist$aupec
#> [1] -1.171875
aupeclist$sd
#> [1] 1.507292