From Causal Discovery to Dynamic Causal Inference in Neural Time Series

ArXi:2603.20980v1 Announce Type: cross Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable. This limits the applicability of dynamic causal inference in many scientific settings.