Inferring causation from time series in Earth system sciences

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, Rick Quax, Markus Reichstein, Marten Scheffer, Bernhard Schölkopf, Peter Spirtes, George Sugihara, Jie Sun, Kun Zhang and Jakob Zscheischler
Additional contact information
Jakob Runge: Institute of Data Science
Sebastian Bathiany: Helmholtz-Zentrum Geesthacht
Erik Bollt: Clarkson University
Gustau Camps-Valls: Universitat de València
Dim Coumou: VU University Amsterdam
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
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
Kun Zhang: Carnegie Mellon University
Jakob Zscheischler: Institute for Atmospheric and Climate Science, ETH Zurich

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: 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.

Date: 2019
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Citations: View citations in EconPapers (21)

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DOI: 10.1038/s41467-019-10105-3

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