Causal Discovery in Nonlinear Dynamical Systems using Koopman Operators

TL;DR

This paper introduces a new data-driven method for causal discovery in nonlinear dynamical systems using Koopman operators, providing theoretical insights and practical algorithms validated on complex systems.

Contribution

It develops a Koopman-based framework for causality in dynamical systems, bridging theory and data-driven causal inference in high-dimensional nonlinear contexts.

Findings

Koopman causality aligns with perturbation-based causal flow.

Demonstrated on Rossler and Lorenz 96 systems.

Provides a practical algorithm for causal analysis.

Abstract

We present a theory of causality in dynamical systems using Koopman operators. Our theory is grounded on a rigorous definition of causal mechanism in dynamical systems given in terms of flow maps. In the Koopman framework, we prove that causal mechanisms manifest as particular flows of observables between function subspaces. While the flow map definition is a clear generalization of the standard definition of causal mechanism given in the structural causal model framework, the flow maps are complicated objects that are not tractable to work with in practice. By contrast, the equivalent Koopman definition lends itself to a straightforward data-driven algorithm that can quantify multivariate causal relations in high-dimensional nonlinear dynamical systems. The coupled Rossler system provides examples and demonstrations throughout our exposition. We also demonstrate the utility of our data-driven Koopman causality measure by identifying causal flow in the Lorenz 96 system. We show that the causal flow identified by our data-driven algorithm agrees with the information flow identified through a perturbation propagation experiment. Our work provides new theoretical insights into causality for nonlinear dynamical systems, as well as a new toolkit for data-driven causal analysis.