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...