PAPD {evalITR} | R Documentation |
Estimation of the Population Average Prescription Difference in Randomized Experiments
Description
This function estimates the Population Average Prescription Difference with a budget constraint. The details of the methods for this design are given in Imai and Li (2019).
Usage
PAPD(T, Thatfp, Thatgp, Y, budget, centered = TRUE)
Arguments
T |
A vector of the unit-level binary treatment receipt variable for each sample. |
Thatfp |
A vector of the unit-level binary treatment that would have been assigned by the first individualized treatment rule. Please ensure that the percentage of treatment units of That is lower than the budget constraint. |
Thatgp |
A vector of the unit-level binary treatment that would have been assigned by the second individualized treatment rule. 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. |
centered |
If |
Value
A list that contains the following items:
papd |
The estimated Population Average Prescription Difference |
sd |
The estimated standard deviation of PAPD. |
Author(s)
Michael Lingzhi Li, Technology and Operations Management, Harvard Business School mili@hbs.edu, 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)
That = c(0,1,1,0,0,1,1,0)
That2 = c(1,0,0,1,1,0,0,1)
Y = c(4,5,0,2,4,1,-4,3)
papdlist <- PAPD(T,That,That2,Y,budget = 0.5)
papdlist$papd
papdlist$sd