Improved Precision in Estimating Average Treatment Effects
Abstract
The Average Treatment Effect (ATE) is a global measure of the effectiveness of an experimental treatment intervention. Classical methods of its estimation either ignore relevant covariates or do not fully exploit them. Moreover, past work has considered covariates as fixed. We present a method for improving the precision of the ATE estimate: the treatment and control responses are estimated via a regression, and information is pooled between the groups to produce an asymptotically unbiased estimate; we subsequently justify the random X paradigm underlying the result. Standard errors are derived, and the estimator's performance is compared to the traditional estimator. Conditions under which the regression-based estimator is preferable are detailed, and a demonstration on real data is presented.
Cite
@article{arxiv.1311.0291,
title = {Improved Precision in Estimating Average Treatment Effects},
author = {Emil Pitkin and Richard Berk and Lawrence Brown and Andreas Buja and Ed George and Kai Zhang and Linda Zhao},
journal= {arXiv preprint arXiv:1311.0291},
year = {2013}
}
Comments
22 pages, 1 figure