causalKNN: Causal KNN and TEP treatment effect estimation

Overview

This package implements the causal KNN and treatment effect projection (TEP) heterogeneous treatment effect estimation algorithms introduced in Hitsch, Misra, and Zhang (2023).

Installation

To install this package in R, run the following commands in R:

install.packages("devtools")
devtools::install_github("walterwzhang/causalKNN", build_vignettes = TRUE)

Alternatively, if you are installing the package from source, run the following commands in R:

install.packages(path_to_file, repos = NULL, type="source")

where path_to_file is the local file path to the repository.

Usage

For how to use this package, please see the Intro Vignette or access it from R:

library(causalKNN)
vignette("causalKNN-Intro")

To bootstrap aggregate the estimators, please follow the Bootstrap Vignette:

vignette("causalKNN-Bootstrap")

Notes

• The changelog can be found in NEWS.md
• The K nearest neighbor index matrices are computed using the knn.index.dist function from the KernelKnn package
• The Elastic-Net (using glmnet) is offered as the treatment effect projection's regression algorithm

References

Hitsch, Guenter J. and Misra, Sanjog and Zhang, Walter, Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation (February 28, 2023). Available at SSRN: https://ssrn.com/abstract=3111957 or http://dx.doi.org/10.2139/ssrn.3111957