RERANDOMIZATION TO IMPROVE COVARIATE BALANCE IN EXPERIMENTS

被引:207
作者
Morgan, Kari Lock [1 ]
Rubin, Donald B. [2 ]
机构
[1] Duke Univ, Dept Stat, Durham, NC 27708 USA
[2] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA
关键词
Randomization; treatment allocation; experimental design; clinical trial; causal effect; Mahalanobis distance; Hotelling's T-2; CLINICAL-TRIALS; CAUSAL INFERENCE; RESTRICTED RANDOMIZATION; ADAPTIVE ALLOCATION; MATCHING METHODS; DESIGN; BIAS; ASSIGNMENT; PURSUIT; SCIENCE;
D O I
10.1214/12-AOS1008
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:1263 / 1282
页数:20
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