Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
This work addresses the problem of reproducible and generalizable model learning in kinetic systems for researchers in fields like biology or chemistry, though it appears incremental as it builds on causal approaches.
The authors tackled the challenge of learning stable and predictive models from noisy, heterogeneous data in kinetic systems by introducing CausalKinetiX, a computationally efficient framework that identifies causal structures. Results showed significant improvements in out-of-sample generalization compared to existing predictive-only methods.
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.