Decoding Multimodal Causality: End-to-End Multimodal Mediation Pathways Inference

The nonparametric identification of causal mediation effectsfrom heterogeneous multimodal observational data remains an opentheoretical problem. Existing single-modal methods cannot handlecross-modal causal relationships, while multimodal learning methodslack causal inference capabilities, preventing reliable mediation pathwayidentification and effect quantification under structural uncertainty.We propose the Multimodal Structure-Informed Guided MediationAnalysis (MM-SIGMA) framework, which achieves automated cross-modal mediation pathway identification and end-to-end uncertaintypropagation through probabilistic causal structure discovery. Weestablish nonparametric identification conditions for the Cross-ModalNatural Direct Effect and Cross-Modal Natural Indirect Effect fromheterogeneous multimodal observational data, and build MM-SIGMAupon this theoretical foundation. Specifically, it employs multimodalvariational autoencoders for causality-preserving latent representation,differentiable Flow-Structural Equation Models for asymptoticallyconsistent latent structure learning, Cross-Modal Path Stability Scoringfor high-confidence pathway identification, and Efficient InfluenceFunctions with Bayesian Model Averaging for end-to-end uncertaintypropagation. Experiments on synthetic data demonstrate state-of-the-art performance under structural uncertainty, nonlinearity, andcross-modal heterogeneity. On the HPP dataset, MM-SIGMA identifiescross-modal mediation pathways connecting sleep, fundus imaging, andcardiovascular health, revealing undiscovered cross-system mechanisms.