让实验效果跨时间迁移，解决旧实验结果在新时间点不适用的问题。
TEA-Time: Transporting Effects Across Time
- 假设时间效应可分离，提出两种跨时间推广方法：重复试验与共用对照组。
- 共用对照组更精确但存在系统偏差，重复试验更可靠且能处理时间依赖关系。
- 适用于需将线上实验结果推广到未来时间点的研究者，如产品迭代与长期评估。
随机对照试验估算的治疗效应不仅局限于研究人群，也受限于试验进行的时间。虽然将实验结果推广到新人群的文献丰富，但跨时间推广的研究较少，且目标估计量的定义并不明确。本文在可分离时间效应假设下形式化了跨时间的平均处理效应，推导出两种识别策略：重复试验和共用对照组，并为每种策略开发了双重稳健、半参数高效的估计器。应用于大型头部A/B测试档案数据，共用对照组策略精度更高，但当时间因素依赖于干预与测量之间的时间间隔而非仅测量时间时，表现出系统性偏差；而重复试验策略允许这种依赖关系，更忠实追踪真实效果。模拟研究分析了每种策略的可靠性边界及隐蔽失效情形。
原文摘要 · Abstract (English)
Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the trial was conducted. The literature on generalizing experimental findings to new populations is extensive, yet transporting effects across time has received far less attention, and even defining the target estimand is nonobvious. We formalize the transported average treatment effect under a separable temporal effects assumption, derive two identification strategies: replicated trials and common arm, and develop doubly robust, semiparametrically efficient estimators for each. Applied to a large archive of headline A/B tests, the common arm strategy is substantially more precise but exhibits systematic bias when the temporal factor depends on the gap between intervention and measurement rather than on measurement time alone, while the replicated trials strategy, which allows this dependence, tracks the ground truth more faithfully. Simulation studies investigate when each strategy is reliable and when it silently fails.
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