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.
翻译:从观察数据得出的因果关系估计是因果关系推论的根本问题之一。然而,大多数估算方法都基于以下强有力的假设:观察到所有混淆者,在现实世界中是不切实际的,也是无法检验的。我们开发了一个调解分析框架,推断潜在混淆者会减少直接和间接因果关系的影响。具体地说,我们引入了普遍结构等式模型,将结构化潜在因素纳入其中,以提高模型对观察数据的良好性,同时分解调解人和结果。拟议框架的一个主要优势是,它利用从起因到结果的因果结构,通过多个调解人来贬低因果影响,而不需要对潜在混淆者的外部信息。此外,拟议框架在整合强大的非参数预测算法的同时,保留可解释的调解效果方面是灵活的。理论上,我们根据拟议的解析方法确定因果关系和调解效果。关于模拟环境的量化实验和一项规范老化研究表明,拟议方法减少了因果关系和调解影响的估计偏差。