Estimation of the Population Average Prescription Effect in Randomized Experiments

This function estimates the Population Average Prescription Effect with and without a budget constraint. The details of the methods for this design are given in Imai and Li (2019).

Usage

PAPE(T, That, Y, budget = NA, centered = TRUE)

Arguments

T: A vector of the unit-level binary treatment receipt variable for each sample.
That: A vector of the unit-level binary treatment that would have been assigned by the individualized treatment rule. If budget is specified, please ensure that the percentage of treatment units of That is lower than the budget constraint.
Y: A vector of the outcome variable of interest for each sample.
budget: The maximum percentage of population that can be treated under the budget constraint. Should be a decimal between 0 and 1. Default is NA which assumes no budget constraint.
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:

pape: The estimated Population Average Prescription Effect.
sd: The estimated standard deviation of PAPE.

References

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

Author

Michael Lingzhi Li, Operations Research Center, Massachusetts Institute of Technology mlli@mit.edu, http://mlli.mit.edu;

Examples

T = c(1,0,1,0,1,0,1,0)
That = c(0,1,1,0,0,1,1,0)
Y = c(4,5,0,2,4,1,-4,3)
papelist <- PAPE(T,That,Y)
papelist$pape
#> [1] -1.857143
papelist$sd
#> [1] 0.7490218