To comprehensively evaluate a public policy intervention, researchers must consider the effects of the policy not just on the implementing region, but also nearby, indirectly-affected regions. For example, an excise tax on sweetened beverages in Philadelphia was shown to not only be associated with a decrease in volume sales of taxed beverages in Philadelphia, but also an increase in sales in bordering counties not subject to the tax. The latter association may be explained by cross-border shopping behaviors of Philadelphia residents and indicate a causal effect of the tax on nearby regions, which may offset the total effect of the intervention. To estimate causal effects in this setting, we extend difference-in-differences methodology to account for such interference between regions and adjust for potential confounding present in quasi-experimental evaluations. Our doubly robust estimators for the average treatment effect on the treated and neighboring control relax standard assumptions on interference and model specification. We apply these methods to evaluate the change in volume sales of taxed beverages in 231 Philadelphia and bordering county stores due to the Philadelphia beverage tax. We also use our methods to explore the heterogeneity of effects across geographic features.

翻译：为全面评估公共政策干预的效果，研究者不仅需关注政策实施地区的效应，还需考虑邻近间接影响地区的效应。例如，费城对含糖饮料征收的消费税不仅与费城本地被征税饮料的销量下降相关，还与未征税边境县饮料销量上升相关。后者可能源于费城居民的跨境消费行为，表明该税收对邻近地区存在因果效应，这可能抵消干预的整体效果。为在此情境下估计因果效应，我们扩展了双重差分方法，以考虑区域间的此类干扰，并调整准实验评估中存在的潜在混杂因素。我们提出的针对处理组和邻近对照组的平均处理效应的双重稳健估计量，放宽了关于干扰和模型设定的标准假设。我们将这些方法应用于评估费城饮料税对231家费城及边境县商店被征税饮料销量变化的影响，并利用方法探索效应在不同地理特征下的异质性。