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

Narayan Puthanmadam Subramaniyam, Reik V. Donner, Davide Caron, Gabriella Panuccio, Jari Hyttinen

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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.

Original languageEnglish
JournalNonlinear Dynamics
DOIs
Publication statusE-pub ahead of print - Jun 2021
Publication typeA1 Journal article-refereed

Keywords

  • Causality
  • Information theory
  • Nonlinear time series analysis
  • Ordinal patterns
  • Transition networks

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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