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. We present a novel approach to identifying and estimating 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 (e.g., risk difference, odds ratio). We develop several estimators of transported effects, including g-computation, inverse odds weighting, and a doubly robust estimator based on the efficient influence function. Simulation results support theoretical properties of the proposed estimators. As an example, we apply our approach to study the effects of a 2018 US federal smoke-free public housing law on air quality in public housing across the US, using data from a DID study conducted in New York City alone.

翻译：差分法是一种在存在未测量混杂因素时识别处理和政策因果效应的常用方法。差分法识别的是处理组样本平均处理效应。然而，此类研究的目标通常是指导处理样本之外目标群体的决策。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；这些方法主要是在基于处理与潜在结果之间条件独立性的识别场景（例如随机试验）中发展和应用的。我们提出了一种基于在研究样本（与目标群体不同）中进行的差分法来识别和估计目标群体中效应的新方法。我们提出了一系列可识别目标群体中因果效应的假设，并采用因果图来说明这些假设。在大多数现实场景中，结果关键依赖于以下假设：任何未测量的混杂因素在关注效应尺度（例如风险差、比值比）上不是效应测量修饰因子。我们开发了多种外推效应的估计量，包括g-计算、逆比值加权以及基于高效影响函数的双重稳健估计量。仿真结果支持所提估计量的理论性质。作为示例，我们应用该方法研究了2018年美国联邦无烟公共住房法对美国各地公共住房空气质量的影响，所使用的数据仅来自在纽约市进行的一项差分研究。