{"ID":2828495,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.14361","arxiv_id":"2512.14361","title":"Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis","abstract":"Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time -- leading to poor performance on irregularly sampled data -- or ignore the underlying causality. We propose CaDyT, a novel method for causal discovery on dynamical systems addressing both these challenges. In contrast to state-of-the-art causal discovery methods that model the problem using discrete-time Dynamic Bayesian networks, our formulation is grounded in Difference-based causal models, which allow milder assumptions for modeling the continuous nature of the system. CaDyT leverages exact Gaussian Process inference for modeling the continuous-time dynamics which is more aligned with the underlying dynamical process. We propose a practical instantiation that identifies the causal structure via a greedy search guided by the Algorithmic Markov Condition and Minimum Description Length principle. Our experiments show that CaDyT outperforms state-of-the-art methods on both regularly and irregularly-sampled data, discovering causal networks closer to the true underlying dynamics.","short_abstract":"Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time -- leading to poor performance on irregularly sampled data -- or ignore the underlying causality. We propo...","url_abs":"https://arxiv.org/abs/2512.14361","url_pdf":"https://arxiv.org/pdf/2512.14361v1","authors":"[\"Nicholas Tagliapietra\",\"Katharina Ensinger\",\"Christoph Zimmer\",\"Osman Mian\"]","published":"2025-12-16T12:41:22Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"math.DS\"]","methods":"[]","has_code":false}