Bivariate partial mapping for detecting causality in complex non-autonomous system

TL;DR

This paper introduces a novel bivariate partial mapping method to detect causality in complex non-autonomous systems by transforming them into autonomous systems, effectively addressing limitations of existing methods.

Contribution

It proposes a new approach that transforms non-autonomous systems into autonomous skew product systems for causality detection, applicable to complex systems with non-manifold phase spaces.

Findings

Successfully detects causality in mathematical models

Effective in real brain activity case

Addresses limitations of existing methods

Abstract

Identifying causality is fundamental for human understanding of the world, where complex non-autonomous systems such as species population changes, brain activities, etc. are extensively existed. Since the phase spaces of such systems are not manifolds, the existing method based on convergent cross mapping is not applicable. This paper proposes a novel bivariate partial mapping method for detecting causality in complex non-autonomous systems. It transforms a non-autonomous system to an autonomous skew product system, and then, by considering the causality changes due to the transformation, detects causality of the original non-autonomous system from the transformed skew product system. The effectiveness of the proposed method is verified by mathematical cases and a real brain activity case, showing that the proposed method successfully detects the causality in complex non-autonomous systems.