ABSTRACT This article considers experimental design based on the strategy of rerandomization to increase the efficiency in experiments. Two aspects of rerandomization are addressed. First, we propose a two-stage allocation sample scheme for randomization inference to the units in experiments that guarantees that the difference-in-mean estimator is an unbiased estimator of the sample average treatment effect for any experiment, conserves the exactness of randomization inference, and halves the time consumption of the rerandomization design. Second, we propose a rank-based covariate-balance measure which can take into account the estimated relative weight of each covariate. Several strategies for estimating these weights using pre-experimental data are proposed. Using Monte Carlo simulations, the proposed strategies are compared to complete randomization and Mahalanobis-based rerandomization. An empirical example is given where the power of a mean difference test of electricity consumption of 54 households is increased by 99%, in comparison to complete randomization, using one of the proposed designs based on high frequency longitudinal electricity consumption data. Supplementary materials for this article are available online.

中文翻译：

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使用实验前纵向数据平衡协变量的重新随机化策略

摘要 本文考虑了基于重新随机化策略的实验设计，以提高实验效率。解决了重新随机化的两个方面。首先，我们提出了一种两阶段分配样本方案，用于对实验中的单元进行随机化推断，以保证均值差估计量是任何实验的样本平均处理效果的无偏估计量，保留了随机化推断的准确性，并将重新随机化设计的时间消耗减半。其次，我们提出了一种基于等级的协变量平衡度量，它可以考虑每个协变量的估计相对权重。提出了几种使用实验前数据估计这些权重的策略。使用蒙特卡罗模拟，将提议的策略与完全随机化和基于 Mahalanobis 的再随机化进行比较。给出了一个经验示例，其中使用基于高频纵向用电量数据的建议设计之一，与完全随机化相比，54 户家庭用电量的均值差异检验的功效提高了 99%。本文的补充材料可在线获取。