Policy interventions can spill over to units of a population that are not directly exposed to the policy but are geographically close to the units receiving the intervention. In recent work, investigations of spillover effects on neighboring regions have focused on estimating the average treatment effect of a particular policy in an observed setting. Our research question broadens this scope by asking what policy consequences would the treated units have experienced under hypothetical exposure settings. When we only observe treated unit(s) surrounded by controls -- as is common when a policy intervention is implemented in a single city or state -- this effect inquires about the policy effects under a counterfactual neighborhood policy status that we do not, in actuality, observe. In this work, we extend difference-in-differences (DiD) approaches to spillover settings and develop identification conditions required to evaluate policy effects in counterfactual treatment scenarios. These causal quantities are policy-relevant for designing effective policies for populations subject to various neighborhood statuses. We develop doubly robust estimators and use extensive numerical experiments to examine their performance under heterogeneous spillover effects. We apply our proposed method to investigate the effect of the Philadelphia beverage tax on unit sales.

翻译：政策干预会通过地理邻近性，对未直接暴露于政策但处于受干预单位附近的群体产生溢出效应。近期研究主要通过观察到的政策场景，考察特定政策对邻近区域的平均处理效应。本研究进一步拓展该视角，探究在假设的暴露情境下，受处理单位本会经历怎样的政策后果。当仅能观测到被对照单位环绕的单个（或少数）受处理单位时（这种情形常见于政策干预在单一城市或州实施的情况），该效应旨在估计反事实邻域政策状态下的政策效果——这种状态在实际中无法被观测。本文在溢出效应设定中扩展了双重差分法（DiD），并开发了评估反事实处理场景中政策效果所需的识别条件。这些因果量对于设计适应不同邻域状态的群体政策具有政策相关性。我们构建了双重稳健估计量，并通过大量数值实验检验其在异质性溢出效应下的表现。最后，我们将所提方法应用于费城含糖饮料税对单位销量的影响分析。