De-confounding Causal Inference Using Latent Multiple-Mediator Pathways
利用潜在多中介路径消除因果推断中的混杂AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论未观测混杂、直接与间接因果效应以及中介效应识别，直接对应因果推断。
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利用潜在多中介路径消除因果推断中的混杂
- 研究问题
- 在观测数据存在未观测混杂时，能否利用处理经多个中介到结果的路径结构，识别并估计直接和间接因果效应？
- 方法与思路
- 作者提出包含结构化潜在因子的广义结构方程框架，同时处理中介与结果的混杂，并允许结合非参数预测算法。
- 材料陈述的贡献
- 摘要称建立了因果与中介效应的识别结果；模拟及正常老龄化研究中的实验显示，该方法降低了相关效应的估计偏差。
- 阅读关注点
- 应核对潜在混杂的识别条件、多中介路径的限制与因子结构，并区分模型拟合改善、效应识别和偏差降低的证据。
- 阅读边界
- 依据 arXiv 2302.05513v1 预印本摘要，不能据此声称已同行评审或期刊接收；识别假设和偏差改善幅度未说明，也不推断刊本变化。
原文摘要
Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates structured latent factors to improve the goodness-of-fit of the model to observed data, and deconfound the mediators and outcome simultaneously. One major advantage of the proposed framework is that it utilizes the causal pathway structure from cause to outcome via multiple mediators to debias the causal effect without requiring external information on latent confounders. In addition, the proposed framework is flexible in terms of integrating powerful nonparametric prediction algorithms while retaining interpretable mediation effects. In theory, we establish the identification of both causal and mediation effects based on the proposed deconfounding method. Numerical experiments on both simulation settings and a normative aging study indicate that the proposed approach reduces the estimation bias of both causal and mediation effects.