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Inferring causation from time series in Earth system sciences

Author

Listed:
  • Jakob Runge

    (Institute of Data Science
    Imperial College)

  • Sebastian Bathiany

    (Helmholtz-Zentrum Geesthacht
    Wageningen University)

  • Erik Bollt

    (Clarkson University)

  • Gustau Camps-Valls

    (Universitat de València)

  • Dim Coumou

    (VU University Amsterdam
    Potsdam Institute for Climate Impact Research, Earth System Analysis)

  • Ethan Deyle

    (University of California, San Diego)

  • Clark Glymour

    (Carnegie Mellon University)

  • Marlene Kretschmer

    (Potsdam Institute for Climate Impact Research, Earth System Analysis)

  • Miguel D. Mahecha

    (Max Planck Institute for Biogeochemistry)

  • Jordi Muñoz-Marí

    (Universitat de València)

  • Egbert H. Nes

    (Wageningen University)

  • Jonas Peters

    (University of Copenhagen)

  • Rick Quax

    (University of Amsterdam
    University of Amsterdam)

  • Markus Reichstein

    (Max Planck Institute for Biogeochemistry)

  • Marten Scheffer

    (Wageningen University)

  • Bernhard Schölkopf

    (Max Planck Institute for Intelligent Systems)

  • Peter Spirtes

    (Carnegie Mellon University)

  • George Sugihara

    (University of California, San Diego)

  • Jie Sun

    (Clarkson University
    Clarkson University)

  • Kun Zhang

    (Carnegie Mellon University)

  • Jakob Zscheischler

    (Institute for Atmospheric and Climate Science, ETH Zurich
    University of Bern
    University of Bern)

Abstract

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

Suggested Citation

  • Jakob Runge & Sebastian Bathiany & Erik Bollt & Gustau Camps-Valls & Dim Coumou & Ethan Deyle & Clark Glymour & Marlene Kretschmer & Miguel D. Mahecha & Jordi Muñoz-Marí & Egbert H. Nes & Jonas Peters, 2019. "Inferring causation from time series in Earth system sciences," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10105-3
    DOI: 10.1038/s41467-019-10105-3
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    Cited by:

    1. Kunxiaojia Yuan & Fa Li & Gavin McNicol & Min Chen & Alison Hoyt & Sara Knox & William J. Riley & Robert Jackson & Qing Zhu, 2024. "Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity," Nature Climate Change, Nature, vol. 14(3), pages 282-288, March.
    2. Jakob Runge, 2023. "Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    3. Andreas Koutsodendris & Vasilis Dakos & William J. Fletcher & Maria Knipping & Ulrich Kotthoff & Alice M. Milner & Ulrich C. Müller & Stefanie Kaboth-Bahr & Oliver A. Kern & Laurin Kolb & Polina Vakhr, 2023. "Atmospheric CO2 forcing on Mediterranean biomes during the past 500 kyrs," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. 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.
    5. van Elteren, Casper & Quax, Rick & Sloot, Peter, 2022. "Dynamic importance of network nodes is poorly predicted by static structural features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    6. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    7. Sourav Mukherjee & Ashok Kumar Mishra & Jakob Zscheischler & Dara Entekhabi, 2023. "Interaction between dry and hot extremes at a global scale using a cascade modeling framework," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    8. Lucile Ricard & Fabrizio Falasca & Jakob Runge & Athanasios Nenes, 2024. "network-based constraint to evaluate climate sensitivity," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    9. Javier, Prince Joseph Erneszer A. & Liponhay, Marissa P. & Dajac, Carlo Vincienzo G. & Monterola, Christopher P., 2022. "Causal network inference in a dam system and its implications on feature selection for machine learning forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    10. Cai, Yunhao & Jing, Peng & Wang, Baihui & Jiang, Chengxi & Wang, Yuan, 2023. "How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    11. Tejasvi Chauhan & Anjana Devanand & Mathew Koll Roxy & Karumuri Ashok & Subimal Ghosh, 2023. "River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    12. Fang Guo & Pei Zhang & Vivian Do & Jakob Runge & Kun Zhang & Zheshen Han & Shenxi Deng & Hongli Lin & Sheikh Taslim Ali & Ruchong Chen & Yuming Guo & Linwei Tian, 2024. "Ozone as an environmental driver of influenza," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    13. Abbas, Khizar & Han, Mengyao & Xu, Deyi & Butt, Khalid Manzoor & Baz, Khan & Cheng, Jinhua & Zhu, Yongguang & Hussain, Sanwal, 2024. "Exploring synergistic and individual causal effects of rare earth elements and renewable energy on multidimensional economic complexity for sustainable economic development," Applied Energy, Elsevier, vol. 364(C).
    14. Schuessler, Julian, 2024. "Causal analysis with observational data," OSF Preprints wam94, Center for Open Science.
    15. Sebastiano Trevisani & Pietro Daniel Omodeo, 2021. "Earth Scientists and Sustainable Development: Geocomputing, New Technologies, and the Humanities," Land, MDPI, vol. 10(3), pages 1-17, March.
    16. Ge, Xinlei & Lin, Aijing, 2023. "Symbolic convergent cross mapping based on permutation mutual information," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    17. Bingbo Gao & Jianyu Yang & Ziyue Chen & George Sugihara & Manchun Li & Alfred Stein & Mei-Po Kwan & Jinfeng Wang, 2023. "Causal inference from cross-sectional earth system data with geographical convergent cross mapping," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    18. Timothy M. Lenton & Jesse F. Abrams & Annett Bartsch & Sebastian Bathiany & Chris A. Boulton & Joshua E. Buxton & Alessandra Conversi & Andrew M. Cunliffe & Sophie Hebden & Thomas Lavergne & Benjamin , 2024. "Remotely sensing potential climate change tipping points across scales," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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