Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample average treatment effect in the treated (SATT). However, a goal of such research is often to inform decision-making in target populations outside the treated sample. Transportability methods have been developed to extend inferences from study samples to external target populations; these methods have primarily been developed and applied in settings where identification is based on conditional independence between the treatment and potential outcomes, such as in a randomized trial. This paper develops identification and estimators for effects in a target population, based on DID conducted in a study sample that differs from the target population. We present a range of assumptions under which one may identify causal effects in the target population and employ causal diagrams to illustrate these assumptions. In most realistic settings, results depend critically on the assumption that any unmeasured confounders are not effect measure modifiers on the scale of the effect of interest. We develop several estimators of transported effects, including a doubly robust estimator based on the efficient influence function. Simulation results support theoretical properties of the proposed estimators. We discuss the potential application of our approach to a study of the effects of a US federal smoke-free housing policy, where the original study was conducted in New York City alone and the goal is extend inferences to other US cities.

翻译：双重差分法（DID）是一种在存在未观测混杂因素时识别处理或政策因果效应的常用方法。通过DID可识别处理组中的样本平均处理效应（SATT），但此类研究的目标通常是为处理样本之外的目标群体决策提供依据。可迁移性方法已被开发用于将研究样本的推断扩展到外部目标群体；这些方法主要基于处理与潜在结果之间的条件独立性假设（如随机试验情境）进行开发和应用。本文旨在基于研究样本（与目标群体存在差异）的DID分析，开发目标群体因果效应的识别方法与估计量。我们提出一系列在目标群体中识别因果效应的假设条件，并采用因果图对这些假设进行阐释。在多数现实场景中，研究结果关键取决于一个假设：即任何未观测混杂因素在心仪效应尺度上均非效应修饰因子。我们开发了多种迁移效应的估计量，包括基于有效影响函数的双稳健估计量。仿真结果验证了所提估计量的理论性质。最后，我们讨论该方法在美国联邦无烟住房政策效应研究中的潜在应用——该原始研究仅限于纽约市，而目标是将推断拓展至美国其他城市。