Inferring causation from time series in Earth system sciences
Jakob Runge(Grantham College), Sebastian Bathiany(German Climate Computing Centre), Erik M. Bollt(Clarkson University), Gustau Camps‐Valls(Parc Científic de la Universitat de València), Dim Coumou(Potsdam Institute for Climate Impact Research), Ethan R. Deyle(Scripps Institution of Oceanography), Clark Glymour(Carnegie Mellon University), Marlene Kretschmer(Potsdam Institute for Climate Impact Research), Miguel D. Mahecha(Max Planck Institute for Biogeochemistry), Jordi Muñoz-Marı́(Parc Científic de la Universitat de València), Egbert H. van Nes(Wageningen University & Research), Jonas Peters(University of Copenhagen), Rick Quax(Netherlands Institute for Advanced Study in the Humanities and Social Sciences), Markus Reichstein(Max Planck Institute for Biogeochemistry), Marten Scheffer(Wageningen University & Research), Bernhard Schölkopf(Max Planck Institute for Intelligent Systems), Peter Spirtes(Carnegie Mellon University), George Sugihara(Scripps Institution of Oceanography), Jie Sun(Clarkson University), Kun Zhang(Carnegie Mellon University), Jakob Zscheischler(University of Bern)
Cited by 926Open Access
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.
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