Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis

• URL: http://arxiv.org/abs/2512.14361v1
• Date: Tue, 16 Dec 2025 12:41:22 GMT
• Title: Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis
• Authors: Nicholas Tagliapietra, Katharina Ensinger, Christoph Zimmer, Osman Mian,
• Abstract summary: Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown.<n>We propose CaDyT, a novel method for causal discovery on dynamical systems.<n>Our experiments show that CaDyT outperforms state-of-the-art methods on both regularly and irregularly-sampled data.
• Score: 7.847876045564289
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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