J
Jakob Runge
Researcher at German Aerospace Center
Publications - 88
Citations - 3970
Jakob Runge is an academic researcher from German Aerospace Center. The author has contributed to research in topics: Causal inference & Computer science. The author has an hindex of 24, co-authored 70 publications receiving 2697 citations. Previous affiliations of Jakob Runge include Humboldt University of Berlin & Humboldt State University.
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Journal ArticleDOI
Inferring causation from time series in Earth system sciences
Jakob Runge,Jakob Runge,Sebastian Bathiany,Erik M. Bollt,Gustau Camps-Valls,Dim Coumou,Dim Coumou,Ethan R. Deyle,Clark Glymour,Marlene Kretschmer,Miguel D. Mahecha,Jordi Muñoz-Marí,Egbert H. van Nes,Jonas Peters,Rick Quax,Markus Reichstein,Marten Scheffer,Bernhard Schölkopf,Peter Spirtes,George Sugihara,Jie Sun,Kun Zhang,Jakob Zscheischler,Jakob Zscheischler,Jakob Zscheischler +24 more
TL;DR: An overview of causal inference frameworks is given, promising applications and methodological challenges are identified, and a causality benchmark platform is initiated to close the gap between method users and developers.
Journal ArticleDOI
Detecting and quantifying causal associations in large nonlinear time series datasets
Jakob Runge,Jakob Runge,Peer Nowack,Marlene Kretschmer,Seth Flaxman,Dino Sejdinovic,Dino Sejdinovic +6 more
TL;DR: In this paper, the authors introduce a method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets.
Journal ArticleDOI
Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy
Jakob Runge,Jakob Runge,Jobst Heitzig,Vladimir Petoukhov,Juergen Kurths,Juergen Kurths,Juergen Kurths +6 more
TL;DR: A formula is presented that decomposes TE into a sum of finite-dimensional contributions that is called decomposed transfer entropy and demonstrates the method's performance using examples of nonlinear stochastic delay-differential equations and observational climate data.
Journal ArticleDOI
Causal network reconstruction from time series: From theoretical assumptions to practical estimation
TL;DR: The problem of inferring causal networks including time lags from multivariate time series is recapitulated from the underlying causal assumptions to practical estimation problems and method performance evaluation approaches and criteria are suggested.
Journal ArticleDOI
Using causal effect networks to analyze different arctic drivers of midlatitude winter circulation
TL;DR: In this article, a causal effect network (CEN) based on graphical models is introduced to assess causal relationships and their time delays between different processes in the Northern Hemisphere midlatitudes.