Granger causality test with nonlinear neural-network-based methods: Python package and simulation study.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 29, 2022

Abstract

BACKGROUND AND OBJECTIVE: Causality defined by Granger in 1969 is a widely used concept, particularly in neuroscience and economics. As there is an increasing interest in nonlinear causality research, a Python package with a neural-network-based causality analysis approach was created. It allows performing causality tests using neural networks based on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), or Multilayer Perceptron (MLP). The aim of this paper is to present the nonlinear method for causality analysis and the created Python package.

Authors

  • Maciej Rosoł
    Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland. Electronic address: maciej.rosol.dokt@pw.edu.pl.
  • Marcel Młyńczak
    Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland.
  • Gerard Cybulski
    Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland.

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