Structural Nested Mean Models Under Parallel Trends Assumptions
平行趋势假设下的结构嵌套均值模型AI 解读 ↓
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平行趋势假设下的结构嵌套均值模型
- 研究问题
- 在处理随时间变化且存在时变混杂时，能否借助平行趋势假设识别比常规时变双重差分更丰富的动态因果效应？
- 方法与思路
- 作者将结构嵌套均值模型与时变双重差分连接，提出条件平行趋势下的识别结果、偏离该假设的敏感性分析及最优策略估计。
- 材料陈述的贡献
- 摘要称，框架覆盖效应异质性、单次处理的持续影响、持续与动态干预及受控直接效应，并用于医保、洪灾和温度应用。
- 阅读关注点
- 应核对条件平行趋势如何处理协变量历史、各因果目标的识别要求，以及最优策略所需的未观测混杂不修饰效应假设。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明识别条件的完整形式、证明及应用估计结果。
原文摘要
We link and extend two approaches to estimating time-varying treatment effects on repeated continuous outcomes--time-varying Difference in Differences (DiD; see Roth et al. (2023) and Chaisemartin et al. (2023) for reviews) and Structural Nested Mean Models (SNMMs; see Vansteelandt and Joffe (2014) for a review). In particular, we show that SNMMs, previously known to be nonparametrically identified under a no unobserved confounding assumption, are also identified under a conditional parallel trends assumption similar to those typically used to justify time-varying DiD methods (but more amenable to time-varying confounding). Because SNMMs model a broader set of causal estimands, our results allow practitioners of time-varying DiD approaches to address additional types of substantive questions under similar assumptions. SNMMs enable estimation of time-varying effect heterogeneity, lasting effects of a `blip' of treatment at a single time point, effects of sustained interventions (possibly on continuous or multi-dimensional treatments) when treatment repeatedly changes value in the data, controlled direct effects, effects of dynamic treatment strategies that depend on covariate history, and more. We provide a method for sensitivity analysis to violations of our parallel trends assumption. We further explain how to estimate optimal treatment regimes via optimal regime SNMMs under parallel trends assumptions plus an assumption that there is no effect modification by unobserved confounders. Finally, we illustrate our methods with real data applications estimating effects of Medicaid expansion on uninsurance rates, effects of floods on flood insurance take-up, and effects of sustained changes in temperature on crop yields.