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Partial cross mapping eliminates indirect causal influences

Author

Listed:
  • Siyang Leng

    (Fudan University
    Fudan University
    University of Tokyo)

  • Huanfei Ma

    (Soochow University)

  • Jürgen Kurths

    (Potsdam Institute for Climate Impact Research
    Saratov State University)

  • Ying-Cheng Lai

    (Arizona State University)

  • Wei Lin

    (Fudan University
    Fudan University
    Fudan University)

  • Kazuyuki Aihara

    (University of Tokyo
    University of Tokyo)

  • Luonan Chen

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    Institute of Brain-Intelligence Technology, Zhangjiang Laboratory
    Chinese Academy of Sciences)

Abstract

Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data.

Suggested Citation

  • Siyang Leng & Huanfei Ma & Jürgen Kurths & Ying-Cheng Lai & Wei Lin & Kazuyuki Aihara & Luonan Chen, 2020. "Partial cross mapping eliminates indirect causal influences," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16238-0
    DOI: 10.1038/s41467-020-16238-0
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    Cited by:

    1. Jakob Runge, 2023. "Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    2. Se Ho Park & Seokmin Ha & Jae Kyoung Kim, 2023. "A general model-based causal inference method overcomes the curse of synchrony and indirect effect," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Xin Li & Qunxi Zhu & Chengli Zhao & Xiaojun Duan & Bolin Zhao & Xue Zhang & Huanfei Ma & Jie Sun & Wei Lin, 2024. "Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Ding Yongmei & Li Yulian, 2024. "Causal Linkage Effect on Chinese Industries via Partial Cross Mapping Under the Background of COVID-19," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1071-1094, March.
    5. Hu, Yunchao & Lu, Guibin & Gao, Wenyu, 2022. "A study on China’s systemically important financial institutions based on multi-time scale causality networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    6. Anwesha Sengupta & Shashankaditya Upadhyay & Indranil Mukherjee & Prasanta K. Panigrahi, 2022. "Describing the effect of influential spreaders on the different sectors of Indian market: a complex networks perspective," Papers 2303.05432, arXiv.org.

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