Inferring causation from time series in Earth system sciences
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Inferring causation from time series in Earth system sciences. / Runge, Jakob; Bathiany, Sebastian; Bollt, Erik; Camps-Valls, Gustau; Coumou, Dim; Deyle, Ethan; Glymour, Clark; Kretschmer, Marlene; Mahecha, Miguel D.; Muñoz-Marí, Jordi; van Nes, Egbert H.; Peters, Jonas; Quax, Rick; Reichstein, Markus; Scheffer, Marten; Schölkopf, Bernhard; Spirtes, Peter; Sugihara, George; Sun, Jie; Zhang, Kun; Zscheischler, Jakob.
I: Nature Communications, Bind 10, Nr. 1, 2553, 2019.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Inferring causation from time series in Earth system sciences
AU - Runge, Jakob
AU - Bathiany, Sebastian
AU - Bollt, Erik
AU - Camps-Valls, Gustau
AU - Coumou, Dim
AU - Deyle, Ethan
AU - Glymour, Clark
AU - Kretschmer, Marlene
AU - Mahecha, Miguel D.
AU - Muñoz-Marí, Jordi
AU - van Nes, Egbert H.
AU - Peters, Jonas
AU - Quax, Rick
AU - Reichstein, Markus
AU - Scheffer, Marten
AU - Schölkopf, Bernhard
AU - Spirtes, Peter
AU - Sugihara, George
AU - Sun, Jie
AU - Zhang, Kun
AU - Zscheischler, Jakob
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067350188&partnerID=8YFLogxK
U2 - 10.1038/s41467-019-10105-3
DO - 10.1038/s41467-019-10105-3
M3 - Journal article
C2 - 31201306
AN - SCOPUS:85067350188
VL - 10
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 2553
ER -
ID: 222971563