A Causal Framework for Evaluating Drivers of Policy Effect Heterogeneity Using Difference-in-Differences

TL;DR

This paper introduces a causal framework for analyzing the drivers behind policy effect heterogeneity using difference-in-differences, enabling more accurate identification of underlying factors influencing policy outcomes.

Contribution

It develops a novel causal approach to evaluate sources of effect heterogeneity within DiD designs, addressing confounding and neighborhood effects.

Findings

Applied framework to Philadelphia beverage tax effects

Identified key drivers of heterogeneity in policy impact

Provided tools for causal effect curve estimation

Abstract

Policymakers and researchers often seek to understand how a policy differentially affects a population and the pathways driving this heterogeneity. For example, when studying an excise tax on sweetened beverages, researchers might assess the roles of cross-border shopping, economic competition, and store-level price changes on beverage sales trends. However, traditional policy evaluation tools, like the difference-in-differences (DiD) approach, primarily target average effects of the observed intervention rather than the underlying drivers of effect heterogeneity. Common approaches to evaluate sources of heterogeneity often lack a causal framework, making it difficult to determine whether observed outcome differences are truly driven by the proposed source of heterogeneity or by other confounding factors. In this paper, we present a framework for evaluating such policy drivers by representing questions of effect heterogeneity under hypothetical interventions and use it to evaluate drivers of the Philadelphia sweetened beverage tax policy effects. Building on recent advancements in estimating causal effect curves under DiD designs, we provide tools to assess policy effect heterogeneity while addressing practical challenges including confounding and neighborhood dynamics.