arXiv Machine Learning By Jonas Braun, Fabian Fischbach, Daniel K\"oglmayr, Sebastian Baur, Christoph R\"ath

Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems

Read the original on arXiv Machine Learning →

arXiv:2608. 17452v1 Announce Type: new Abstract: Machine learning methods predict many real-world systems with remarkable accuracy, but they are typically treated as black boxes that offer no insight into which interactions drive the dynamics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 5

From Causal Discovery to Dynamic Causal Inference in Neural Time Series

arXiv:2603. 20980v3 Announce Type: replace Abstract: 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.

By Dmitry Zaytsev, Valentina Kuskova, Michael Coppedge
arXiv Machine Learning
Jul 14

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).

By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang
arXiv Machine Learning
Jun 10

Interpretable deep convolutional model for nonlinear multivariate time series in complex systems

arXiv:2501. 04339v2 Announce Type: replace-cross Abstract: We introduce the Deep Convolutional Interpreter for Time Series (DCIts), a deep-learning architecture for nonlinear multivariate time series that provides sample-specific, locally interpretable descriptions of the underlying interaction structure.

By Domjan Baric, Davor Horvatic