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.

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)

References in corpus (2)

• Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique
• On regression adjustments in experiments with several treatments

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• Insights on Variance Estimation for Blocked and Matched Pairs Designs
• On finite-population Bayesian inferences for $2^K$ factorial designs with binary outcomes
• Sharp variance estimator and causal bootstrap in stratified randomized experiments