Abstract 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, this effect inquires about the policy effects under a counterfactual neighborhood policy status that we do not, in actuality, observe. In this talk, I will introduce difference-in-differences methods 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 apply our proposed approach to examine the impact of the Philadelphia beverage tax on unit sales. I will discuss its potential extension for identifying substitution effects.