Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
未测量混杂下因果直接效应的识别与估计AI 解读 ↓
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未测量混杂下因果直接效应的识别与估计
- 研究问题
- 在暴露与中介之间存在未观测基线混杂的非随机研究中，能否放宽常用假设而仍识别自然直接效应？
- 方法与思路
- 作者提出放宽的识别条件，讨论弱化跨世界反事实独立性的方法，并利用半参数效率理论构造灵活的多重稳健估计。
- 材料陈述的贡献
- 摘要称，论文为特定未观测混杂情形提供自然直接效应的识别结果，并将其用于疫苗研究中的免疫保护机制分析。
- 阅读关注点
- 应核对被放宽与仍保留的独立性条件、多重稳健性的具体组合，以及疫苗研究中的效应定义和机制解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未列出完整识别条件，不能理解为任意未测量混杂均可处理。
原文摘要
Causal mediation analysis provides techniques for defining and estimating effects that may be endowed with mechanistic interpretations. With many scientific investigations seeking to address mechanistic questions, causal direct and indirect effects have garnered much attention. The natural direct and indirect effects, the most widely used among such causal mediation estimands, are limited in their practical utility due to stringent identification requirements. Accordingly, considerable effort has been invested in developing alternative direct and indirect effect decompositions with relaxed identification requirements. Such efforts often yield effect definitions with nuanced and challenging interpretations. By contrast, relatively limited attention has been paid to relaxing the identification assumptions of the natural direct and indirect effects. Motivated by a secondary aim of a recent non-randomized vaccine prospective cohort study (NCT05168813), we present a set of relaxed conditions under which the natural direct effect is identifiable in spite of unobserved baseline confounding of the exposure-mediator pathway; we use this result to investigate the effect mediated by putative immune correlates of protection. Relaxing the commonly used but restrictive cross-world counterfactual independence assumption, we discuss strategies for evaluating the natural direct effect in non-randomized settings that arise in the analysis of vaccine studies. We revisit prior studies of semi-parametric efficiency theory to demonstrate the construction of flexible, multiply robust estimators of the natural direct effect and discuss efficient estimation strategies that do not place restrictive modeling assumptions on nuisance functions.