Decomposition, identification and multiply robust estimation of natural mediation effects with multiple mediators
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多中介自然中介效应的分解、识别与多重稳健估计
- 研究问题
- 当多个中介变量的因果顺序未指定时，如何分解自然间接效应、识别各分量，并处理估计中条件密度模型的相容性？
- 方法与思路
- 作者将自然间接效应分为各中介的出口间接效应与剩余交互项，推导效率界，并通过联结函数重参数化构造相容模型。
- 材料陈述的贡献
- 摘要称，估计量在四类模型误设下仍一致且渐近正态，并具有局部半参数有效性；另提出稳定化版本和样本分割扩展。
- 阅读关注点
- 应核对分解的因果解释、识别假设、四类误设组合，以及联结函数参数化如何保证模型相容和样本分割需要哪些条件。
- 阅读边界
- 仅据所给Crossref摘要，无法判断识别假设的可信度；四重稳健性针对特定误设组合，不能解释为任意模型错误均无影响。
原文摘要
Summary Natural mediation effects are desirable estimands for studying causal mechanisms in a population, but complications arise in defining and estimating natural indirect effects through multiple mediators with an unspecified causal ordering. We propose a decomposition of the natural indirect effect of multiple mediators into individual components, termed exit indirect effects, and a remainder interaction term, and study the similarities to and differences from existing natural and interventional effects in the literature. We provide a set of identification assumptions for estimating all components of the proposed natural effect decomposition and derive the semiparametric efficiency bounds for the effects. The efficient influence functions contain conditional densities that are variationally dependent, which is uncommon in existing problems and may lead to model incompatibility. By ensuring model compatibility through a reparameterization based on copulas, our estimator is quadruply robust, which means that it remains consistent and asymptotically normal under four types of possible misspecification, and also is locally semiparametric efficient. We further propose a stabilized quadruply robust estimator to improve practical performance under possibly misspecified models, as well as a nonparametric extension based on sample splitting.