Inferring Causality from Noisy Time Series Data - A Test of Convergent Cross-Mapping
Year: 2016
Type: conference-paper
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.
Authors: Dan Mønster, Riccardo Fusaroli, Kristian Tylén, Andreas Roepstorff, Jacob F. Sherson
Institution: Aarhus University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Funder: Aarhus Universitet
OpenAlex ID: W2290526893