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Causal Discovery of Stochastic Dynamical Systems: A Markov Chain Approach

Author

Listed:
  • Marcell Stippinger

    (Wigner Research Centre for Physics, H-1121 Budapest, Hungary)

  • Attila Bencze

    (Wigner Research Centre for Physics, H-1121 Budapest, Hungary
    Electrophysics Institute, Óbuda University, H-1034 Budapest, Hungary)

  • Ádám Zlatniczki

    (Department of Computer Science and Information Theory, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
    Ericsson Hungary, H-1117 Budapest, Hungary)

  • Zoltán Somogyvári

    (Wigner Research Centre for Physics, H-1121 Budapest, Hungary
    Axoncord Ltd., H-1048 Budapest, Hungary)

  • András Telcs

    (Wigner Research Centre for Physics, H-1121 Budapest, Hungary
    Department of Computer Science and Information Theory, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
    Department of Quantitative Methods, Faculty of Business and Economics, University of Pannonia, H-8200 Veszprém, Hungary)

Abstract

Our proposed method for exploring the causal discovery of stochastic dynamic systems is designed to overcome the limitations of existing methods in detecting hidden and common drivers. The method is based on a simple principle and is presented in a nonparametric structural vector autoregressive modeling framework.

Suggested Citation

  • Marcell Stippinger & Attila Bencze & Ádám Zlatniczki & Zoltán Somogyvári & András Telcs, 2023. "Causal Discovery of Stochastic Dynamical Systems: A Markov Chain Approach," Mathematics, MDPI, vol. 11(4), pages 1-21, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:852-:d:1060777
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    References listed on IDEAS

    as
    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.
    2. Mariusz Maziarz, 2015. "A review of the Granger-causality fallacy," The Journal of Philosophical Economics, Bucharest Academy of Economic Studies, The Journal of Philosophical Economics, vol. 8(2), May.
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    Cited by:

    1. Benkő, Zsigmond & Zlatniczki, Ádám & Stippinger, Marcell & Fabó, Dániel & Sólyom, András & Erőss, Loránd & Telcs, András & Somogyvári, Zoltán, 2024. "Bayesian inference of causal relations between dynamical systems," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).

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