Detecting and quantifying causal associations in large nonlinear time series datasets J Runge, P Nowack, M Kretschmer, S Flaxman, D Sejdinovic Science advances 5 (11), eaau4996, 2019 | 702 | 2019 |
Inferring causation from time series in Earth system sciences J Runge, S Bathiany, E Bollt, G Camps-Valls, D Coumou, E Deyle, ... Nature communications 10 (1), 2553, 2019 | 687 | 2019 |
Causal network reconstruction from time series: From theoretical assumptions to practical estimation J Runge Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7), 2018 | 353 | 2018 |
Escaping the curse of dimensionality in estimating multivariate transfer entropy J Runge, J Heitzig, V Petoukhov, J Kurths Physical review letters 108 (25), 258701, 2012 | 346 | 2012 |
Identifying causal gateways and mediators in complex spatio-temporal systems J Runge, V Petoukhov, JF Donges, J Hlinka, N Jajcay, M Vejmelka, ... Nature communications 6 (1), 8502, 2015 | 292 | 2015 |
Using causal effect networks to analyze different Arctic drivers of midlatitude winter circulation M Kretschmer, D Coumou, JF Donges, J Runge Journal of climate 29 (11), 4069-4081, 2016 | 277 | 2016 |
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information J Runge Proceedings of the 21st International Conference on Artificial Intelligence …, 2018 | 188 | 2018 |
Quantifying the strength and delay of climatic interactions: The ambiguities of cross correlation and a novel measure based on graphical models J Runge, V Petoukhov, J Kurths Journal of climate 27 (2), 720-739, 2014 | 187 | 2014 |
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets J Runge Conference on Uncertainty in Artificial Intelligence, 1388-1397, 2020 | 174 | 2020 |
Quantifying causal coupling strength: A lag-specific measure for multivariate time series related to transfer entropy J Runge, J Heitzig, N Marwan, J Kurths Physical Review E 86 (6), 061121, 2012 | 157 | 2012 |
Reliability of inference of directed climate networks using conditional mutual information J Hlinka, D Hartman, M Vejmelka, J Runge, N Marwan, J Kurths, M Paluš Entropy 15 (6), 2023-2045, 2013 | 127 | 2013 |
Momentary information transfer as a coupling measure of time series B Pompe, J Runge Physical Review E 83 (5), 051122, 2011 | 118 | 2011 |
The different stratospheric influence on cold-extremes in Eurasia and North America M Kretschmer, J Cohen, V Matthias, J Runge, D Coumou npj Climate and Atmospheric Science 1 (1), 44, 2018 | 114 | 2018 |
Causal networks for climate model evaluation and constrained projections P Nowack, J Runge, V Eyring, JD Haigh Nature communications 11 (1), 1415, 2020 | 108 | 2020 |
Disentangling different types of El Niño episodes by evolving climate network analysis A Radebach, RV Donner, J Runge, JF Donges, J Kurths Physical Review E 88 (5), 052807, 2013 | 105 | 2013 |
Turn down the heat: climate extremes, regional impacts, and the case for resilience. HJ Schellnhuber, B Hare, O Serdeczny, M Schaeffer, S Adams, F Baarsch, ... | 104 | 2013 |
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package JF Donges, J Heitzig, B Beronov, M Wiedermann, J Runge, QY Feng, ... Chaos: An Interdisciplinary Journal of Nonlinear Science 25 (11), 2015 | 99 | 2015 |
Statistical mechanics and information-theoretic perspectives on complexity in the earth system G Balasis, RV Donner, SM Potirakis, J Runge, C Papadimitriou, IA Daglis, ... Entropy 15 (11), 4844-4888, 2013 | 98 | 2013 |
High-recall causal discovery for autocorrelated time series with latent confounders A Gerhardus, J Runge Advances in Neural Information Processing Systems 33, 12615-12625, 2020 | 87 | 2020 |
Early prediction of extreme stratospheric polar vortex states based on causal precursors M Kretschmer, J Runge, D Coumou Geophysical research letters 44 (16), 8592-8600, 2017 | 76 | 2017 |