Comparison of six methods for the detection of causality in a bivariate time series

Physical Review. E
Anna KrakovskáMilan Palus

Abstract

In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.

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Citations

Sep 19, 2020·Physical Review. E·Anna Krakovská, Jozef Jakubík
Aug 3, 2018·Chaos·Milan PalušMartina Chvosteková
May 22, 2019·Physical Review. E·David DiegoBjarte Hannisdal
Jul 3, 2020·Chaos·Yu HuangChristian L E Franzke
Aug 11, 2019·Entropy·Teddy CraciunescuMichela Gelfusa
Jan 21, 2021·Physical Review. E·Dmitry A Smirnov
Nov 21, 2020·Physical Review. E·George StepaniantsJ Nathan Kutz
Apr 21, 2021·Scientific Reports·Riccardo Silini, Cristina Masoller
Oct 24, 2021·Scientific Reports·Theodoros BermperidisElizabeth B Torres

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