In designing and evaluating public policies, policymakers and researchers often hypothesize about the ways in which a policy may differentially affect a population and aim to assess these pathways in practice. For example, when studying unhealthy food or beverage excise taxes, researchers might explore how cross-border shopping (i.e., spillover), economic competition (i.e., interference) and store-level price changes influence sales. However, policy evaluation designs, including the difference-in-differences (DiD) approach, traditionally target the average effect of the intervention rather than the underlying drivers of heterogeneity. Extensions of these approaches to evaluate drivers of policy effect heterogeneity often involve exploratory subgroup analyses or outcome models parameterized by driver-specific variables.  However, neither approach investigates potential drivers within a causal framework, limiting the analysis to associative relationships between drivers and outcomes, which may be confounded by differences among sub-populations exposed to varying levels of the drivers. Therefore, rigorous policy evaluation requires robust methods to adjust for confounding and accommodate the interconnected relationship between stores within competitive economic landscapes.