州级政策异质性分析：考量与建议
State policy heterogeneity analyses: considerations and proposals
AI总结：
针对州级政策异质性分析中因果估计量与推断目标不匹配的问题，提出将边界化个体处理效应（ITE）作为替代推断目标，并在双重差分框架下开发方法，通过模拟和实证案例展示其优于条件平均处理效应（CATE）估计。
摘要：
州级政策研究经常进行异质性分析，量化处理效应如何随州特征变化。这些分析可用于为特定州的政策决策提供信息，或推断政策效应如何与其他州特征结合而变化。然而，在具有不同背景和政策环境、多种版本相似政策以及差异化政策实施的州级环境中，这些分析所针对的因果量可能无法与推断目标对齐。本文通过区分与州政策环境中异质性分析相关的几个因果估计量来澄清这些问题，包括州特定处理效应（ITE）、条件平均处理效应（CATE）和受控直接效应（CDE）。我们认为CATE通常最容易识别和估计，但可能不是最政策相关的推断目标。此外，将不同政策或实施粗化为单一指标的普遍做法进一步复杂化了这些分析的解释。受这些局限性的启发，我们提出将边界化ITE作为替代推断目标，在明确假设下为每个州的政策效应提供范围，这些假设量化了与理想识别条件的偏差。这些边界针对一个定义明确且政策相关的量，即特定州的效应。我们在双重差分框架内开发了这种方法，并讨论了如何使用预处理数据来告知敏感性参数。通过模拟，我们证明边界化州特定效应比CATE估计更能可靠地确定ITE的符号。然后，我们通过该方法检验《平价医疗法案》医疗补助扩展对高剂量丁丙诺啡处方的影响。
英文摘要：
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.
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