What Transfers Across Cities? Invariant Representation Learning for Robust Spatio-Temporal Forecasting

Cross-city forecasting requires transferring knowledge across urban environments that share predictive structure yet differ in traffic dynamics, observation density, and latent environmental factors. Many existing approaches align source and target distributions, but alignment can also retain source-specific correlations and induce negative transfer when domain mismatch becomes substantial. We introduce CauCrossNet, an invariant representation transfer framework that separates relatively stable predictive structure from transient environment-specific variation and promotes prediction consistency across alternative stable-space summaries. The learned invariant space provides a basis for target adaptation. Target-side augmentation is conditioned on stable representations, while source knowledge is integrated according to representation-level transfer reliability rather than unconditional similarity. This design is causality-inspired but does not assume an identifiable structural causal model or require interventional observations. Across multiple city pairs, we evaluate standard transfer, limited-target-data adaptation, temporal shifts, and longer horizons. Results show that selective invariant transfer improves forecasting across source-target relations, remains effective under mismatch and limited target data, and reduces degradation from poorly matched source information through reliability-aware integration. These findings suggest that robust cross-city forecasting depends not only on reducing domain discrepancy, but also on identifying predictive structure that remains stable, useful, and transferable across environments. Code and experimental configurations are available at the anonymous repository: https://anonymous.4open.science/r/CauCrossNet-040B.