AUPECcv: Estimation of the Area Under Prescription Evaluation Curve...

In evalITR: Evaluating Individualized Treatment Rules

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

Description

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.

Author(s)

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

References

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

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$aupec
aupeclist$sd

evalITR documentation built on Aug. 26, 2023, 1:08 a.m.