Jonas Peters

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Joris M. MooijProfessor in Mathematical Statistics, Korteweg-de Vries Institute, University of Amsterdam (NL)Verified email at uva.nl
Nicolai MeinshausenProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichVerified email at stat.math.ethz.ch
Niklas PfisterAssociate Professor, University of CopenhagenVerified email at math.ku.dk
Kun ZhangCarnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at cmu.edu
Jakob ZscheischlerHelmholtz Centre for Environmental Research - UFZVerified email at ufz.de
Rune ChristiansenPostdoc in statistics, University of CopenhagenVerified email at math.ku.dk
Rajen D. ShahUniversity of CambridgeVerified email at cam.ac.uk
Leon BottouFacebook AI ResearchVerified email at bottou.org
Sorawit SaengkyongamAppleVerified email at ethz.ch
Martin Emil JakobsenUniversity of CopenhagenVerified email at math.ku.dk
Nikolaj ThamsPhD studentVerified email at math.ku.dk
Sebastian WeichwaldUniversity of Copenhagen / formerly Max Planck Institute for Intelligent Systems, ETH ZurichVerified email at math.ku.dk
David SontagProfessor, Massachusetts Institute of TechnologyVerified email at csail.mit.edu
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Susan MurphyHarvard UniversityVerified email at g.harvard.edu
Stefan BauerHelmholtz | TUM | CIFARVerified email at tum.de

Jonas Peters

Professor of Statistics, ETH Zurich

Verified email at stat.math.ethz.ch - Homepage

Causality Statistics Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Elements of causal inference: foundations and learning algorithms J Peters, D Janzing, B Schölkopf The MIT Press, 2017	2651	2017
Nonlinear causal discovery with additive noise models P Hoyer, D Janzing, JM Mooij, J Peters, B Schölkopf Advances in neural information processing systems 21, 2008	1330	2008
Causal inference using invariant prediction: identification and confidence intervals J Peters, P Bühlmann, N Meinshausen Journal of the Royal Statistical Society, Series B (with discussion) 78 (5 …, 2016	1177	2016
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	902	2019
Counterfactual reasoning and learning systems: The example of computational advertising L Bottou, J Peters, J Quiñonero-Candela, D Charles, M Chickering, ... Journal of Machine Learning Research 14 (Léon Bottou, Jonas Peters, Joaquin …, 2013	858	2013
Kernel-based conditional independence test and application in causal discovery K Zhang, J Peters, D Janzing, B Schölkopf 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI …, 2012	788	2012
On causal and anticausal learning B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij 29th International Conference on Machine Learning (ICML 2012), 1255-1262, 2012, 2012	736	2012
Causal discovery with continuous additive noise models J Peters, JM Mooij, D Janzing, B Schölkopf The Journal of Machine Learning Research 15, 2009-2053, 2014	680	2014
Distinguishing cause from effect using observational data: methods and benchmarks JM Mooij, J Peters, D Janzing, J Zscheischler, B Schölkopf Journal of Machine Learning Research 17 (32), 1-102, 2016	621	2016
Invariant models for causal transfer learning M Rojas-Carulla, B Schölkopf, R Turner, J Peters Journal of Machine Learning Research 19 (36), 1-34, 2018	427	2018
Identifiability of Gaussian structural equation models with equal error variances J Peters, P Bühlmann Biometrika 101 (1), 219-228, 2014	407	2014
The hardness of conditional independence testing and the generalised covariance measure RD Shah, J Peters	400	2020
CAM: Causal additive models, high-dimensional order search and penalized regression P Bühlmann, J Peters, J Ernest	383	2014
Invariant causal prediction for nonlinear models C Heinze-Deml, J Peters, N Meinshausen Journal of Causal Inference 6 (2), 20170016, 2018	320	2018
Anchor regression: Heterogeneous data meet causality D Rothenhäusler, N Meinshausen, P Bühlmann, J Peters Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2021	260	2021
Foundations of structural causal models with cycles and latent variables S Bongers, P Forré, J Peters, JM Mooij The Annals of Statistics 49 (5), 2885-2915, 2021	240*	2021
Causal inference on time series using restricted structural equation models J Peters, D Janzing, B Schölkopf Advances in neural information processing systems 26, 2013	240	2013
Kernel-based tests for joint independence N Pfister, P Bühlmann, B Schölkopf, J Peters Journal of Royal Statistical Society, Series B 80, 5-31, 2017	232	2017
The three major axes of terrestrial ecosystem function M Migliavacca, T Musavi, MD Mahecha, JA Nelson, J Knauer, ... Nature 598 (7881), 468-472, 2021	221	2021
Causal inference on discrete data using additive noise models J Peters, D Janzing, B Scholkopf IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (12), 2436 …, 2011	211	2011

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