Inferring causation from time series in Earth system sciences

Runge, Jakob; Bathiany, Sebastian; Bollt, Erik M.; Camps‐Valls, Gustau; Coumou, Dim; Deyle, Ethan R.; Glymour, Clark; Kretschmer, Marlene; Mahecha, Miguel D.; Muñoz-Marı́, Jordi; Nes, Egbert H. van; Peters, Jonas; Quax, Rick; Reichstein, Markus; Scheffer, Marten; Schölkopf, Bernhard; Spirtes, Peter; Sugihara, George; Sun, Jie; Zhang, Kun; Zscheischler, Jakob

doi:10.1038/s41467-019-10105-3

reviewNature CommunicationsJun 14, 2019GOLD OA

Inferring causation from time series in Earth system sciences

JRJakob Runge SBSebastian Bathiany EMErik M. Bollt GCGustau Camps‐Valls DCDim Coumou

Grantham College · Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) · +21 more institutions

PubMed

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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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925

total citations

FWCI: 60.43
Percentile: 100%
References: 101

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Authors

21

Topics & keywords

Topics

Keywords

Causation
Series (stratigraphy)
Time series
Data science
Computer science
Computational biology
Biology
Epistemology

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