Non-linear correlations underlie linear response and causality

Abstract

The inference of causal relationships among observed variables is a pivotal, longstanding problem in the scientific community. An intuitive method for quantifying these causal links involves examining the response of one variable to perturbations in another. The fluctuation-dissipation theorem elegantly connects this response to the correlation functions of the unperturbed system, thereby bridging the concepts of causality and correlation. However, this relationship becomes intricate in nonlinear systems, where knowledge of the invariant measure is required but elusive, especially in high-dimensional spaces. In this study, we establish a novel link between the Koopman operator of nonlinear stochastic systems and the response function. This connection provides an alternative method for computing the response function using generalized correlation functions, even when the invariant measure is unknown. We validate our theoretical framework by applying it to a nonlinear high-dimensional system amenable to exact solutions, demonstrating convergence and consistency with established results. Finally, we discuss a significant interplay between the resulting causal network and the relevant time scales of the system.