A simple and successful shrinkage method for weighting estimators of treatment effects

Computational Statistics & Data Analysis512-525100
https://doi.org/10.1016/j.csda.2014.09.015

Abstract

A simple shrinkage method is proposed to improve the performance of weighting estimators of the average treatment effect. As the weights in these estimators can become arbitrarily large for the propensity scores close to the boundaries, three different variants of a shrinkage method for the propensity scores are analyzed. The results of a comprehensive Monte Carlo study demonstrate that this simple method substantially reduces the mean squared error of the estimators in finite samples, and is superior to several popular trimming approaches over a wide range of settings.

Keywords

Average treatment effect; Econometric evaluation; Penalizing; Propensity score; Shrinkage

Co authors
Winfried Pohlmeier
R. Seiberlich
© 2020 by Derya Uysal.