Mitigating Unmeasured Confounding Bias in Large-Scale Causal Mediation Analysis Via Factor Analysis

Thomas M. Menino Convention & Exhibition Center 

Large-scale causal mediation analysis performs a large number of mediation analyses to assess potential causal pathways between an exposure and an outcome, e.g., testing each DNA methylation site as a potential mediator in epigenome-wide studies. Traditional mediation analysis approaches typically rely on the strong assumption of no unmeasured confounding between the mediators and the outcome. This assumption is often violated in observational studies where neither the exposure nor the mediators are randomized, leading to potentially biased inference on mediation effects. To address this challenge, we propose a Factor Analysis-based Mediation Analysis (FAMA) framework that corrects for unmeasured confounding in large-scale mediation analysis settings. FAMA integrates factor analysis with methods for handling omitted-variable bias to estimate natural indirect effects in the presence of unmeasured confounding. We establish the theoretical validity of our approach and demonstrate its robust performance in a variety of confounding scenarios through extensive simulation studies. We further apply FAMA to the analysis of the epigenome-wide Normative Aging Study to investigate the medi

Causal mediation analysis;

Factor Analysis;

Large-scale inference;

Unmeasured Confounders. 

Biometrics Section