| Nonlinear causal discovery with additive noise models PO Hoyer, D Janzing, JM Mooij, J Peters, B Schölkopf Advances in neural information processing systems, 689-696, 2009 | 520 | 2009 |
| Elements of causal inference J Peters, D Janzing, B Schölkopf The MIT Press, 2017 | 404 | 2017 |
| Counterfactual reasoning and learning systems: The example of computational advertising L Bottou, J Peters, J Quiñonero-Candela, DX Charles, DM Chickering, ... The Journal of Machine Learning Research 14 (1), 3207-3260, 2013 | 357 | 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 | 300 | 2012 |
| Distinguishing cause from effect using observational data: methods and benchmarks JM Mooij, J Peters, D Janzing, J Zscheischler, B Schölkopf The Journal of Machine Learning Research 17 (1), 1103-1204, 2016 | 248 | 2016 |
| 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 | 234 | 2014 |
| 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 | 216 | 2016 |
| 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 | 204 | 2012 |
| 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 | 130 | 2011 |
| Identifiability of Gaussian structural equation models with equal error variances J Peters, P Bühlmann Biometrika 101 (1), 219-228, 2014 | 128* | 2014 |
| CAM: Causal additive models, high-dimensional order search and penalized regression P Bühlmann, J Peters, J Ernest The Annals of Statistics 42 (6), 2526-2556, 2014 | 110 | 2014 |
| Regression by dependence minimization and its application to causal inference in additive noise models J Mooij, D Janzing, J Peters, B Schölkopf 26th annual international conference on machine learning (ICML), 745-752, 2009 | 96 | 2009 |
| Identifiability of causal graphs using functional models J Peters, J Mooij, D Janzing, B Schölkopf 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI …, 2012 | 91 | 2012 |
| 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 | 74 | 2017 |
| Invariant models for causal transfer learning M Rojas-Carulla, B Schölkopf, R Turner, J Peters The Journal of Machine Learning Research 19 (1), 1309-1342, 2018 | 70* | 2018 |
| 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), 1-13, 2019 | 68 | 2019 |
| Causal inference on time series using restricted structural equation models J Peters, D Janzing, B Schölkopf Advances in Neural Information Processing Systems, 154-162, 2013 | 66 | 2013 |
| Methods for causal inference from gene perturbation experiments and validation N Meinshausen, A Hauser, JM Mooij, J Peters, P Versteeg, P Bühlmann Proceedings of the National Academy of Sciences 113 (27), 7361-7368, 2016 | 60 | 2016 |
| Identifying cause and effect on discrete data using additive noise models J Peters, D Janzing, B Schölkopf 13th International Conference on Artificial Intelligence and Statistics, 597-604, 2010 | 54 | 2010 |
| Anchor regression: heterogeneous data meets causality D Rothenhäusler, N Meinshausen, P Bühlmann, J Peters arXiv preprint arXiv:1801.06229, 2018 | 50 | 2018 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
