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SHARP BOUNDS ON THE VARIANCE IN RANDOMIZED EXPERIMENTS
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DOI：10.1214/13-AOS1200.png)
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摘要
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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.
Keyword:
Causal inference
finite populations
potential outcomes
randomized experiments
variance estimation
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