Sharp bounds on the variance in randomized experiments
Peter M. Aronow, Donald P. Green, Donald K. K. Lee
The Annals of Statistics · 2014 · Volume 42 · Issue 3
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.
Keywords
62A01, 62D99, 62G15, Causal inference, finite populations, potential outcomes, randomized experiments, variance estimation
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