  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.
