Post-Treatment Confounding in Causal Mediation Studies: A Cutting-Edge Problem and A Novel Solution via Sensitivity Analysis
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因果中介研究中的处理后混杂与敏感性分析
- 研究问题
- 当中介与结局之间存在处理后混杂，且混杂变量在反事实处理状态下不可观测时，如何评估自然直接与间接效应的敏感性？
- 方法与思路
- 作者在所述序贯可忽略性下，将实际与反事实混杂变量的条件相关性作为敏感性参数，通过填补或积分开展加权中介分析。
- 材料陈述的贡献
- 摘要称方法在不增加额外识别假设的情况下，随合理相关性范围生成效应界限，适用于二元和连续混杂变量。
- 阅读关注点
- 核对序贯可忽略性的准确表述、反事实条件分布的构造、相关性范围的依据，以及效应界限是否还依赖参数模型。
- 阅读边界
- 所据为 arXiv 预印本，不能确认同行评审或期刊接收；摘要未完整说明分布设定与识别条件，不能理解为无需假设即可识别。
原文摘要
In causal mediation studies that decompose an average treatment effect into a natural indirect effect (NIE) and a natural direct effect (NDE), examples of post-treatment confounding are abundant. Past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due to incomplete information: it is observed under the actual treatment condition while missing under the counterfactual treatment condition. This study proposes a new sensitivity analysis strategy for handling post-treatment confounding and incorporates it into weighting-based causal mediation analysis without making extra identification assumptions. Under the sequential ignorability of the treatment assignment and of the mediator, we obtain the conditional distribution of the post-treatment confounder under the counterfactual treatment as a function of not just pretreatment covariates but also its counterpart under the actual treatment. The sensitivity analysis then generates a bound for the NIE and that for the NDE over a plausible range of the conditional correlation between the post-treatment confounder under the actual and that under the counterfactual conditions. Implemented through either imputation or integration, the strategy is suitable for binary as well as continuous measures of post-treatment confounders. Simulation results demonstrate major strengths and potential limitations of this new solution. A re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data reveals that the initial analytic results are sensitive to omitted post-treatment confounding.