Inferring Causality from Noisy Time Series Data - A Test of Convergent Cross-Mapping

Dan Mønster, Riccardo Fusaroli, Kristian Tylén, Andreas Roepstorff, Jacob F. Sherson

Abstract

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses.

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Paper Citation


in Harvard Style

Mønster D., Fusaroli R., Tylén K., Roepstorff A. and Sherson J. (2016). Inferring Causality from Noisy Time Series Data - A Test of Convergent Cross-Mapping . In Proceedings of the 1st International Conference on Complex Information Systems - Volume 1: COMPLEXIS, ISBN 978-989-758-181-6, pages 48-56. DOI: 10.5220/0005932600480056


in Bibtex Style

@conference{complexis16,
author={Dan Mønster and Riccardo Fusaroli and Kristian Tylén and Andreas Roepstorff and Jacob F. Sherson},
title={Inferring Causality from Noisy Time Series Data - A Test of Convergent Cross-Mapping},
booktitle={Proceedings of the 1st International Conference on Complex Information Systems - Volume 1: COMPLEXIS,},
year={2016},
pages={48-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005932600480056},
isbn={978-989-758-181-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Complex Information Systems - Volume 1: COMPLEXIS,
TI - Inferring Causality from Noisy Time Series Data - A Test of Convergent Cross-Mapping
SN - 978-989-758-181-6
AU - Mønster D.
AU - Fusaroli R.
AU - Tylén K.
AU - Roepstorff A.
AU - Sherson J.
PY - 2016
SP - 48
EP - 56
DO - 10.5220/0005932600480056