Factor-Adjusted Multiple Testing for High-Dimensional Individual Mediation Effects

2026-02-18

AI Summary

This study addresses the challenge that dependence among mediators in high-dimensional mediation analysis often invalidates conventional debiased inference and inflates the false discovery rate (FDR). To overcome this, we propose the Factor-Adjusted Debiasing Mediation Test (FADMT), a novel framework that introduces factor adjustment into high-dimensional mediation analysis for the first time. By modeling an approximate factor structure in the mediator error terms, FADMT extracts latent common factors and constructs decorrelated pseudo-mediators, enabling large-scale inference on individual mediation effects with theoretical guarantees for FDR control. Under mild high-dimensional conditions, the method establishes asymptotic normality of the resulting estimators. Simulations demonstrate its superior finite-sample performance across diverse dependence structures, and its practical utility is validated through applications to TCGA-BRCA multi-omics data and stock market interconnectivity analysis.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Matrix & Tensor Methods
• Multiagent Systems: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Identifying individual mediators is a central goal of high-dimensional mediation analysis, yet pervasive dependence among mediators can invalidate standard debiased inference and lead to substantial false discovery rate (FDR) inflation. We propose a Factor-Adjusted Debiased Mediation Testing (FADMT) framework that enables large-scale inference for individual mediation effects with FDR control under complex dependence structures. Our approach posits an approximate factor structure on the unobserved errors of the mediator model, extracts common latent factors, and constructs decorrelated pseudo-mediators for the subsequent inferential procedure. We establish the asymptotic normality of the debiased estimator and develop a multiple testing procedure with theoretical FDR control under mild high-dimensional conditions. By adjusting for latent factor induced dependence, FADMT also improves robustness to spurious associations driven by shared latent variation in observational studies. Extensive simulations demonstrate the superior finite-sample performance across a wide range of correlation structures. Applications to TCGA-BRCA multi-omics data and to China's stock connect study further illustrate the practical utility of the proposed method.

Problem

• high-dimensional mediation analysis
• individual mediators
• dependence among mediators
• false discovery rate inflation
• latent factors

Innovation

• Factor-Adjusted Debiased Mediation Testing
• high-dimensional mediation analysis
• false discovery rate control
• latent factor structure
• decorrelated pseudo-mediators