顺序重随机化切换实验
Sequentially-Rerandomized Switchback Experiments
AI总结：
本文提出顺序重随机化切换实验(SRSB)，通过在每个时间周期重新随机化处理以平衡预定义的预测变量，提升实验精度并应对非平稳性和跨期效应。
摘要：
大规模在线平台和市场系统常通过在多个时间段内对操作单元(如地理区域、地区或集群)随机化处理来评估新政策。在这些设置中，由于单元数量有限、跨单元异质性、非平稳性和潜在的跨期影响，标准A/B测试可能效率低下或不可靠。我们提出顺序重随机化切换实验(SRSB)，一种新的实验设计，通过在每个时间周期重新随机化处理以强制平衡预定义的预测变量，构建自过去观察中。在无跨期影响的情况下，SRSB通过平衡滞后结果和协变量利用时间依赖性来提高精度；我们开发了在尖锐空虚假设下的有限样本随机化推断以及随着时期数量增长的渐近推断。然后，我们将SRSB扩展到具有一阶跨期影响的设置，并引入一种按先前处理定义的strata进行重新随机化的阻塞SRSB变体，以形成稳定且可比的
英文摘要：
Large-scale online platforms and marketplace systems often evaluate new policies through experiments that randomize treatment across operational units (e.g., geographies, regions, or clusters) over many time periods. In these settings, standard A/B testing can be inefficient or unreliable due to a limited number of units, substantial cross-unit heterogeneity, non-stationarity, and potential carryover across periods. We propose Sequentially-Rerandomized Switchback Experiments (SRSB), a new experimental design that helps mitigate these challenges. SRSB re-randomizes treatment at each time period such as to enforce balance on pre-specified prognostic variables constructed from past observations. In the absence of carryover, SRSB improves precision by leveraging temporal dependence through balancing lagged outcomes and covariates; we develop finite-sample randomization inference under a sharp null as well as asymptotic inference as the number of periods grows. We then extend SRSB to settings with first-order carryover and introduce a blocked SRSB variant that rerandomizes within strata defined by the previous treatment to form stable and comparable "stay" groups. Extensive simulations demonstrate the practical gains and robustness of SRSB relative to standard switchback designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
