Building causation links in stochastic nonlinear systems from data
The work uses machine learning within response theory to detect causal links in complex stochastic nonlinear systems from observational data. It develops methods to estimate how one variable influences another by modeling system responses to perturbations, enabling causal inference without needing known evolution laws or lab experiments.