| Elements of causal inference: foundations and learning algorithms J Peters, D Janzing, B Schölkopf The MIT Press, 2017 | 2433 | 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 | 1261 | 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 | 1100 | 2016 |
| 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 | 840 | 2013 |
| 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 | 817 | 2019 |
| 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 | 736 | 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 | 701 | 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 | 626 | 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 | 585 | 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 | 399 | 2018 |
| Identifiability of Gaussian structural equation models with equal error variances J Peters, P Bühlmann Biometrika 101 (1), 219-228, 2014 | 379 | 2014 |
| CAM: Causal additive models, high-dimensional order search and penalized regression P Bühlmann, J Peters, J Ernest | 362 | 2014 |
| The hardness of conditional independence testing and the generalised covariance measure RD Shah, J Peters | 360 | 2020 |
| Invariant causal prediction for nonlinear models C Heinze-Deml, J Peters, N Meinshausen Journal of Causal Inference 6 (2), 20170016, 2018 | 301 | 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 | 245 | 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 | 231 | 2013 |
| 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 | 218* | 2021 |
| 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 | 217 | 2017 |
| 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 | 200 | 2011 |
| The three major axes of terrestrial ecosystem function M Migliavacca, T Musavi, MD Mahecha, JA Nelson, J Knauer, ... Nature 598 (7881), 468-472, 2021 | 196 | 2021 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHtuebingen.mpg.de의 이메일 확인됨
