RetiSEM: Generalising Causal Models for Fragmented Biomedical Data

RetiSEM is a newly proposed domain-constrained structural equation modeling (SEM) framework designed for causal graph recovery and mediation analysis in fragmented biomedical data. It organizes variables into biologically informed blocks, applies forbidden-edge constraints, and decomposes effects into total, natural direct, and natural indirect components. In evaluations across ten synthetic benchmarks and a real-world dataset, RetiSEM demonstrated lower structural error and improved causal accuracy compared to unconstrained baselines, making it a valuable tool for practitioners in biomedical AI facing incomplete data.