Deciphering interventional dynamical causality via Kolmogorov-Arnold networks.

Identifying causal relationships from observational time-series data is a central challenge in understanding complex dynamical systems. However, few existing methods attempt to capture the intuitive human understanding of causality: that an intervention on one variable leads to a subsequent change in another. To bridge this gap, this paper introduces a causal discovery method named Intervened TaylorKAN (ITK), which simulates controlled experiments within reconstructed systems. Specifically, we leverage the recently emerging Kolmogorov-Arnold Networks (KANs) with Taylor series as activation functions to construct a surrogate model that faithfully replicates the original system's dynamics. The core of ITK involves applying targeted interventions on this reconstructed system and observing the responses in other variables, thereby providing more intuitive causal inferences. We evaluated ITK's performance through extensive experiments on both synthetic and real-world datasets. Results demonstrate that ITK achieves high accuracy on synthetic data with known causal structures, confirming its validity. When applied to real-world data, ITK produces reasonable and interpretable causal conclusions, highlighting its potential to decipher causal relationships in complex natural systems.