State policy heterogeneity analyses: considerations and proposals
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州级政策异质性分析：问题辨析与方法建议
- 研究问题
- 州级政策背景、版本和执行方式不同时，异质性分析究竟针对州特定效应、条件平均效应还是受控直接效应，是否匹配政策目标？
- 方法与思路
- 作者区分相关因果目标量，在双重差分框架下对州特定效应构造界，并以敏感性参数刻画偏离理想识别条件的程度。
- 材料陈述的贡献
- 摘要称，模拟中州特定效应的界比条件平均效应估计更可靠地判断效应符号，并以医疗补助扩张对丁丙诺啡处方的影响示例。
- 阅读关注点
- 应核对州特定效应的定义、政策版本合并的影响、界所依赖的限制，以及处理前数据如何为敏感性参数提供信息。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出界的公式、识别偏离尺度或模拟细节，符号判断优势并非普遍保证。
原文摘要
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.