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Orca: Neural Operators for Causal Reasoning in Continuous Time

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Structural causal models are the standard language for reasoning about interventions and counterfactuals, but they describe static variables, typically measured once, and usually forbid cyclic dependencies. Many systems we care about, such as patients, climates, and economies, instead evolve continuously in time, are observed at irregular time points, and contain feedback loops.

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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