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To comprehensively evaluate a public policy intervention, researchers mustconsider the effects of the policy not just on the implementing region, butalso nearby, indirectly-affected regions. For example, an excise tax onsweetened beverages in Philadelphia was shown to not only be associated with adecrease in volume sales of taxed beverages in Philadelphia, but also anincrease in sales in bordering counties not subject to the tax. The latterassociation may be explained by cross-border shopping behaviors of Philadelphiaresidents and indicate a causal effect of the tax on nearby regions, which mayoffset the total effect of the intervention. To estimate causal effects in thissetting, we extend difference-in-differences methodology to account for suchinterference between regions and adjust for potential confounding present inquasi-experimental evaluations. Our doubly robust estimators for the averagetreatment effect on the treated and neighboring control relax standardassumptions on interference and model specification. We apply these methods toevaluate the change in volume sales of taxed beverages in 231 Philadelphia andbordering county stores due to the Philadelphia beverage tax. We also use ourmethods to explore the heterogeneity of effects across geographic features.

Estimation of Policy-Relevant Causal Effects in the Presence of Interference with an Application to the Philadelphia Beverage Tax