Rerandomization discards undesired treatment assignments to ensure covariate balance in randomized experiments. However, rerandomization based on acceptance-rejection sampling is computationally inefficient, especially when numerous independent assignments are required to perform randomization-based statistical inference. Existing acceleration methods are suboptimal and are not applicable in structured experiments, including stratified experiments and experiments with clusters. Based on metaheuristics in combinatorial optimization, we propose a novel variable neighborhood searching rerandomization(VNSRR) method to draw balanced assignments in various experiments efficiently. We derive the unbiasedness and a lower bound for the variance reduction of the treatment effect estimator under VNSRR. Simulation studies and a real data example indicate that our method maintains the appealing statistical properties of rerandomization and can sample thousands of treatment assignments within seconds, even in cases where existing methods require an hour to complete the task.

翻译：重随机化通过剔除不理想的处理分配方案，确保随机化实验中协变量的均衡性。然而，基于接受-拒绝抽样的重随机化方法计算效率低下，尤其当需要大量独立分配方案以执行基于随机化的统计推断时更为突出。现有加速方法存在次优性，且无法适用于结构化实验（包括分层实验和含群组实验）。基于组合优化中的元启发式算法，我们提出了一种新颖的变邻域搜索重随机化（VNSRR）方法，可在各类实验中高效生成均衡分配方案。我们推导了VNSRR下处理效应估计量的无偏性及其方差缩减的下界。仿真研究与实际数据案例表明，该方法保持了重随机化优越的统计性质，且能在数秒内采样数千个处理分配方案——即便在现有方法需耗时一小时完成的场景下亦可实现。