Causal Discovery of Stochastic Dynamical Systems: A Markov Chain Approach

Marcell Stippinger (), Attila Bencze, Ádám Zlatniczki, Zoltán Somogyvári and András Telcs ()
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Marcell Stippinger: Wigner Research Centre for Physics, H-1121 Budapest, Hungary
Attila Bencze: Wigner Research Centre for Physics, H-1121 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
Zoltán Somogyvári: Wigner Research Centre for Physics, H-1121 Budapest, Hungary
András Telcs: Wigner Research Centre for Physics, H-1121 Budapest, Hungary

Mathematics, 2023, vol. 11, issue 4, 1-21

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.

Keywords: causality; hidden driver; Markov chains; hidden common cause (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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