Sharp bounds on the variance in randomized experiments
Abstract
We propose a consistent estimator of sharp bounds on the variance of the difference-in-means estimator in completely randomized experiments. Generalizing Robins [Stat. Med. 7 (1988) 773-785], our results resolve a well-known identification problem in causal inference posed by Neyman [Statist. Sci. 5 (1990) 465-472. Reprint of the original 1923 paper]. A practical implication of our results is that the upper bound estimator facilitates the asymptotically narrowest conservative Wald-type confidence intervals, with applications in randomized controlled and clinical trials.
Cite
@article{arxiv.1405.6555,
title = {Sharp bounds on the variance in randomized experiments},
author = {Peter M. Aronow and Donald P. Green and Donald K. K. Lee},
journal= {arXiv preprint arXiv:1405.6555},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.1214/13-AOS1200 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)