State-level policy studies often conduct heterogeneity analyses that quantify how treatment effects vary across state characteristics. These analyses may be used to inform state-specific policy decisions, or to infer how the effect of a policy changes in combination with other state characteristics. However, in state-level settings with varied contexts and policy landscapes, multiple versions of similar policies, and differential policy implementation, the causal quantities targeted by these analyses may not align with the inferential goals. This paper clarifies these issues by distinguishing several causal estimands relevant to heterogeneity analyses in state-policy settings, including state-specific treatment effects (ITE), conditional average treatment effects (CATE), and controlled direct effects (CDE). We argue that the CATE is often the easiest to identify and estimate, but may not be the most policy relevant target of inference. Moreover, the widespread practice of coarsening distinct policies or implementations into a single indicator further complicates the interpretation of these analyses. Motivated by these limitations, we propose bounding ITEs as an alternative inferential goal, yielding ranges for each state's policy effect under explicit assumptions that quantify deviations from the ideal identifying conditions. These bounds target a well-defined and policy-relevant quantity, the effect for specific states. We develop this approach within a difference-in-differences framework and discuss how sensitivity parameters may be informed using pre-treatment data. Through simulations we demonstrate that bounding state-specific effects can more reliably determine the sign of the ITEs than CATE estimates. We then illustrate this method to examine the effect of the Affordable Care Act Medicaid expansion on high-volume buprenorphine prescribing.

翻译：州级政策研究常进行异质性分析，以量化处理效应如何随州特征变化。此类分析可用于指导特定州的政策决策，或推断政策效应如何与其他州特征共同变化。然而，在州级情境中，由于背景与政策环境各异、相似政策存在多种版本以及政策实施存在差异，这些分析所针对的因果量可能与其推断目标不一致。本文通过区分州政策情境中与异质性分析相关的若干因果估计量（包括州特定处理效应、条件平均处理效应及受控直接效应），厘清了这些问题。我们认为条件平均处理效应通常最易于识别与估计，但可能并非最具政策相关性的推断目标。此外，将不同政策或实施方案粗粒度化为单一指标的普遍做法，进一步增加了这些分析的解释难度。基于这些局限性，我们提出以界定州特定处理效应边界作为替代推断目标，在明确量化与理想识别条件偏差的假设下，为各州政策效应生成区间范围。这些边界针对的是定义明确且具政策相关性的量——特定州的政策效应。我们在双重差分框架内构建了该方法，并讨论了如何利用处理前数据为敏感性参数提供依据。通过模拟实验，我们证明相较于条件平均处理效应估计，界定州特定效应能更可靠地确定州特定处理效应的符号。最后，我们应用该方法分析了《平价医疗法案》医疗补助扩展对高剂量丁丙诺啡处方开具的影响。