A Causal Framework for Evaluating Heterogeneous Policy Mechanisms Using Difference-in-Differences

In designing and evaluating public policies, policymakers and researchers often hypothesize about the mechanisms through which a policy may affect a population and aim to assess these mechanisms in practice. For example, when studying an excise tax on sweetened beverages, researchers might explore how cross-border shopping, economic competition, and store-level price changes differentially affect store sales. However, many policy evaluation designs, including the difference-in-differences (DiD) approach, traditionally target the average effect of the intervention rather than the underlying mechanisms. Extensions of these approaches to evaluate policy mechanisms often involve exploratory subgroup analyses or outcome models parameterized by mechanism-specific variables. However, neither approach studies the mechanisms within a causal framework, limiting the analysis to associative relationships between mechanisms and outcomes, which may be confounded by differences among sub-populations exposed to varying levels of the mechanisms. Therefore, rigorous mechanism evaluation requires robust techniques to adjust for confounding and accommodate the interconnected relationship between stores within competitive economic landscapes. In this paper, we present a framework for evaluating policy mechanisms by studying Philadelphia beverage tax. Our approach builds on recent advancements in causal effect curve estimators under DiD designs, offering tools and insights for assessing policy mechanisms complicated by confounding and network interference.