hettx-package: Fisherian and Neymanian Methods for Detecting and Measuring...

In hettx: Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

Description

This package implements methods developed by Ding, Feller, and Miratrix (JRSS-B, 2016) "Randomization Inference for Treatment Effect Variation", for validly testing whether there is unexplained variation in treatment effects across observations. The package also implements methods introduced in Ding, Feller, and Miratrix (JASA, 2019) "Decomposing Treatment Effect Variation", for measuring the degree of treatment effect heterogeneity explained by covariates. The package includes wrapper functions implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the tests.

Details

This package partially supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D150040. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

Special thanks to Masha Bertling for some early work on documenting this project.

Author(s)

Peng Ding, Avi Feller, Ben Fifield, and Luke Miratrix

Maintainer: Ben Fifield [e-mail]

References

Ding, Peng, Avi Feller and Luke Miratrix. (2016) "Randomization Inference for Treatment Effect Variation", Journal of the Royal Statistical Society-Series B. Ding, Peng, Avi Feller and Luke Miratrix. (2019) "Decomposing Treatment Effect Variation", Journal of the American Statistical Association.

hettx documentation built on Aug. 20, 2023, 1:06 a.m.