Journal
ANNALS OF STATISTICS
Volume 40, Issue 2, Pages 1263-1282Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/12-AOS1008
Keywords
Randomization; treatment allocation; experimental design; clinical trial; causal effect; Mahalanobis distance; Hotelling's T-2
Categories
Funding
- [NSF SES-0550887]
- [NSF IIS-1017967]
- [NIH R01DA23879]
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Randomized experiments are the gold standard for estimating causal effects, yet often in practice, chance imbalances exist in covariate distributions between treatment groups. If covariate data are available before units are exposed to treatments, these chance imbalances can be mitigated by first checking covariate balance before the physical experiment takes place. Provided a precise definition of imbalance has been specified in advance, unbalanced randomizations can be discarded, followed by a rerandomization, and this process can continue until a randomization yielding balance according to the definition is achieved. By improving covariate balance, rerandomization provides more precise and trustworthy estimates of treatment effects.
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