Department of Economics and Business Economics

Inferring causality from noisy time series data: A test of Convergent Cross-Mapping

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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.
Original languageEnglish
Title of host publicationProceedings of the 1st International Conference on Complex Information Systems
EditorsVíctor Méndez Muñoz, Oleg Gusikhin, Victor Chang
PublisherSCITEPRESS Digital Library
Publication year2016
Pages48-56
ISBN (print)978-989-758-181-6
DOIs
StatePublished - 2016
Event1st International Conference on Complex Information Systems - Rome, Italy

Conference

Conference1st International Conference on Complex Information Systems
Nummer1
LandItaly
ByRome
Periode22/04/201624/04/2016
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