Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6869160 | Computational Statistics & Data Analysis | 2016 | 14 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Winfried Pohlmeier, Ruben Seiberlich, Selver Derya Uysal,