Learning stable and predictive structures in kinetic systems

Research output: Contribution to journalJournal articleResearchpeer-review


Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well-established approaches focusing solely on predictive performance, especially for out-of-sample generalization
Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number51
Pages (from-to)25405-25411
Publication statusPublished - 2019

    Research areas

  • kinetic systems, causal inference, stability, invariance, structure learning

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