Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

Xiong Ying Fudan University Si-Yang Leng Fudan University Huan-Fei Ma Soochow University Qing Nie University of California, Irvine Ying-Cheng Lai Arizona State University Wei Lin Fudan University

Dynamical Systems Data Analysis, Bio-Statistics, Bio-Mathematics Data Analysis mathscidoc:2207.11002

RESEARCH, 2022, 9870149, 2022.5
Data-based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.
dynamical causality; time series; dynamical system; scaling
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@inproceedings{xiong2022continuity,
  title={Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately},
  author={Xiong Ying, Si-Yang Leng, Huan-Fei Ma, Qing Nie, Ying-Cheng Lai, and Wei Lin},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220731151415146848721},
  booktitle={RESEARCH},
  volume={2022},
  pages={9870149},
  year={2022},
}
Xiong Ying, Si-Yang Leng, Huan-Fei Ma, Qing Nie, Ying-Cheng Lai, and Wei Lin. Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately. 2022. Vol. 2022. In RESEARCH. pp.9870149. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220731151415146848721.
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