针对偏倚的非参数平衡方法用于稳定的因果中介分析
Bias-Targeted Nonparametric Balancing for Stable Causal Mediation Analysis
AI总结：
本文提出一种非参数加权平衡方法，用于稳定因果中介分析中的估计器，通过重新参数化似然函数减少连续中介变量带来的偏倚和方差。
摘要：
基于影响函数（IF）的估计器在中介分析中被广泛应用，因其模型灵活性，但标准实现需要直接估计中介变量和治疗变量的分布函数。由于这些函数出现在IF估计器的分母中，它们可能导致显著的不稳定性，特别是在连续中介变量的情况下。在本文中，我们提出了一种基于似然重新参数化的替代实现方法，适用于单中介和多中介情况。关键思想是根据其在IF估计器偏倚结构中的作用来估计涉及的噪声函数。在我们的方法中，潜在导致不稳定的噪声函数通过一种新的非参数加权平衡方法进行估计——可以视为协变量平衡广义方法在中介分析中的非参数扩展——完全稳定了估计器。我们建立了在适当正则条件下的一致性、多重鲁棒性和渐近正态性。模拟研究显示，与现有方法相比，在连续中介变量的情况下，偏倚和方差显著降低。我们进一步使用NHANES 2013-2014数据集来说明该方法，以估计肥胖对冠心病的中介效应，中介变量为糖化血红蛋白。
英文摘要：
Influence function (IF)-based estimators are widely used in mediation analysis due to their modeling flexibility, but standard implementations require direct estimation of the distribution functions of the mediator and treatment variables. Since these functions appear in the denominator of IF-based estimators, they can induce significant instability, particularly with continuous mediators. In this work, we propose an alternative implementation of IF-based estimators for both single- and multiple-mediator settings, based on reparametrizations of the likelihood. The key idea is to estimate the involved nuisance functions according to their role in the bias structure of the IF-based estimators. In our approach, key nuisance functions that are potential sources of instability are estimated using a novel nonparametric weighted balancing method-which can be viewed as a nonparametric extension of covariate balancing generalized to mediation analysis-fully stabilizing the estimators. We establish consistency and multiple robustness under suitable regularity conditions, and asymptotic normality. Simulation studies demonstrate substantial reductions in bias and variance relative to existing methods for continuous mediators. We further illustrate the approach using NHANES 2013-2014 data to estimate the effect of obesity on coronary heart disease mediated by Glycohemoglobin.
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