Proximal Inference for Indirect and Intervening Effects in Population Interventions
人口干预中间接效应与干预变量效应的近端推断AI 解读 ↓
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人口干预中间接效应与干预变量效应的近端推断
- 研究问题
- 当暴露、中介和结果之间均存在未测混杂，且暴露干预可能定义不清时，如何识别并估计政策相关的间接效应与干预变量效应？
- 方法与思路
- 作者利用观测协变量作为代理，在近端因果推断框架内提出三种识别策略，推导半参数效率界，并构造多重稳健、局部有效估计量。
- 材料陈述的贡献
- 摘要称该框架统一处理两类目标效应，允许部分模型误设；作者以模拟评估有限样本表现，并应用于饮酒经人格解体症状影响抑郁风险的问题。
- 阅读关注点
- 应核对三种策略各自的代理变量条件、桥函数与识别要求，多重稳健所对应的模型组合，以及两类目标效应的政策解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出代理有效性的具体条件，应用也不能据此确认未测混杂已被消除。
原文摘要
Unmeasured confounding, unethical exposure, and ill-defined interventions pose significant challenges to evaluating policy-relevant mediation estimands in medicine and public health. In observational studies involving harmful exposures, the population intervention indirect effect (PIIE) is often more salient than the natural indirect effect, as the latter relies on hypothetical interventions that may be ethically or practically unfeasible. While the PIIE can be identified via the generalized front-door criterion under unmeasured exposure-outcome confounding, existing estimation methods typically assume the absence of unmeasured confounding for the mediator. Furthermore, when the exposure corresponds to ill-defined interventions, the standard PIIE criterion fails; however, the generalized front-door formula may still identify the causal effect of an intervening variable designed to capture the indirect effect. This paper develops a unified identification and estimation framework for the PIIE and the causal effect of an intervening variable in settings with pervasive unmeasured confounding affecting exposure-mediator, exposure-outcome, and mediator-outcome relationships. Specifically, we leverage observed covariates as proxy variables to construct three distinct identification strategies within a proximal causal inference framework. We characterize the semiparametric efficiency bound for the target estimands and develop multiply robust, locally efficient estimators that remain consistent under partial model misspecification. The finite-sample performance of our estimators is demonstrated through simulations. Finally, we apply our methodology to study the indirect effect of alcohol consumption on depression risk as mediated by depersonalization symptoms.