Treatment effects estimated from randomized controlled trials are local not only to the study population but also to the time at which the trial was conducted. We develop a framework for temporal transportation: extrapolating treatment effects to time periods where no experiment was conducted. We target the transported average treatment effect (TATE) and show that under a separable temporal effects assumption, the TATE decomposes into an observed average treatment effect and a temporal ratio. We provide two identification strategies -- one using replicated trials comparing the same treatments at different times, another using common treatment arms observed across time -- and develop doubly robust, semiparametrically efficient estimators for each. Monte Carlo simulations confirm that both estimators achieve nominal coverage, with the common arm strategy yielding substantial efficiency gains when its stronger assumptions hold. We apply our methods to A/B tests from the Upworthy Research Archive, demonstrating that the two strategies exhibit a variance-bias tradeoff: the common arm approach offers greater precision but may incur bias when treatments interact heterogeneously with temporal factors.

翻译：从随机对照试验中估计的处理效应不仅局限于研究群体，也局限于试验实施的时间。我们开发了一个时间传输框架：将处理效应外推至未进行实验的时间段。我们以传输平均处理效应为目标，并证明在可分离时间效应假设下，TATE可分解为观测到的平均处理效应与一个时间比率。我们提供了两种识别策略——一种使用在不同时间比较相同处理的重复试验，另一种使用跨时间观测的共有处理组——并为每种策略开发了双重稳健、半参数有效的估计量。蒙特卡洛模拟证实两种估计量均达到名义覆盖率，其中共有处理组策略在其更强假设成立时可带来显著的效率提升。我们将方法应用于来自Upworthy研究档案的A/B测试，证明两种策略呈现方差-偏差权衡：共有处理组方法精度更高，但当处理与时间因素存在异质性交互时可能产生偏差。