Causal coupling inference from multivariate time series based on ordinal partition transition networks

TL;DR

This paper introduces a novel method using ordinal partition transition networks to infer causal relationships and coupling delays among multiple dynamical systems, validated on simulations and electrophysiological data.

Contribution

It generalizes OPTNs for multivariate data, enabling reliable detection of causality and delays in complex systems and real-world neural data.

Findings

Successfully identified causal directions in simulated systems

Detected coupling delays accurately in nonlinear systems

Revealed causal structures in neural electrophysiology data

Abstract

Identifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.