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.

翻译：政策制定者和研究者常常希望理解政策如何对人口产生差异化影响，以及驱动这种异质性的路径。例如，在研究含糖饮料消费税时，研究者可能会评估跨境购物、经济竞争和商店层面价格变化对饮料销售趋势的作用。然而，传统政策评估工具（如双重差分法）主要关注观测干预的平均效应，而非效应异质性的内在驱动因素。评估异质性来源的常用方法往往缺乏因果框架，难以确定观测到的结果差异究竟是由所提出的异质性来源驱动，还是由其他混杂因素导致。本文提出一个评估此类政策驱动因素的框架，通过在假设干预下表征效应异质性问题，并将其应用于费城含糖饮料税收政策效应的驱动因素评估。基于双重差分设计下估计因果效应曲线的最新进展，我们提供了评估政策效应异质性的工具，同时解决了包括混杂效应和邻里动态在内的实际挑战。