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Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy

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  • Fatimah Abdul Razak
  • Henrik Jeldtoft Jensen

Abstract

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.

Suggested Citation

  • Fatimah Abdul Razak & Henrik Jeldtoft Jensen, 2014. "Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-14, June.
    • Handle: RePEc:plo:pone00:0099462
    DOI: 10.1371/journal.pone.0099462
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    References listed on IDEAS

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    1. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    1. Angeliki Papana & Catherine Kyrtsou & Dimitris Kugiumtzis & Cees Diks, 2017. "Assessment of resampling methods for causality testing: A note on the US inflation behavior," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-20, July.
    2. Carlos F Alvarez & Luis E Palafox & Leocundo Aguilar & Mauricio A Sanchez & Luis G Martinez, 2016. "Using Link Disconnection Entropy Disorder to Detect Fast Moving Nodes in MANETs," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-15, May.
    3. Coulibaly, Saliya & Bessin, Florent & Clerc, Marcel G. & Mussot, Arnaud, 2022. "Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    4. Augusto Anguita-Ruiz & Alberto Segura-Delgado & Rafael Alcalá & Concepción M Aguilera & Jesús Alcalá-Fdez, 2020. "eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-34, April.
    5. Xiaogeng Wan & Lanxi Xu, 2018. "A study for multiscale information transfer measures based on conditional mutual information," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-30, December.
    6. Irving O Morales & Emmanuel Landa & Carlos Calderon Angeles & Juan C Toledo & Ana Leonor Rivera & Joel Mendoza Temis & Alejandro Frank, 2015. "Behavior of Early Warnings near the Critical Temperature in the Two-Dimensional Ising Model," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.

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