Beyond Predictive Invariance: Causal Mechanism Transport for Generalizable Brain Network Analysis

Brain network analysis is challenged by substantial distribution shifts arising from population and acquisition heterogeneity across imaging sites. Existing domain generalization methods improve robustness by seeking predictive regularities shared across source domains, while site heterogeneity can arise through distinct biological and measurement pathways. To characterize transportability under these structured variations, we introduce Causal Mechanism Transport (CMT), which models the two pathways in a multi-site selection diagram and derives a conditional criterion for transportable disease-related representations. To operationalize this criterion from observed multi-site data, we develop Causal Information Contrast (CIC), which contrasts representation distributions before and after conditioning on site within comparable disease and biological contexts. Instantiated with conditional Cauchy-Schwarz divergence, CIC provides a differentiable training objective. Experiments spanning controlled biological and measurement shifts, multiple real-world generalization settings, and independent representation audits across five neuroimaging cohorts demonstrate consistent gains across architectures and reduced conditional site dependence.