evalHTE (version 0.1.1)

URATE: This function use individualized treatment rule to identify exceptional responders. The details of the methods for this design are given in Imai and Li (2023).

Description

This function use individualized treatment rule to identify exceptional responders. The details of the methods for this design are given in Imai and Li (2023).

Usage

URATE(D, tau, Y)

Value

A list that contains the following items:

rate

The estimated vector of URATE of length Y.

sd

The estimated vector of standard deviation of URATE.

Arguments

D

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

tau

A vector of the unit-level continuous score. Conditional Average Treatment Effect is one possible measure.

Y

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

Author

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

References

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

Examples

D <- c(1, 0, 1, 0, 1, 0, 1, 0)
tau <- c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7)
Y <- c(4, 5, 0, 2, 4, 1, -4, 3)
ratelist <- URATE(D, tau, Y)
ratelist$rate
ratelist$sd