| Elements of causal inference: foundations and learning algorithms J Peters, D Janzing, B Schölkopf The MIT Press, 2017 | 2289 | 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 | 1227 | 2008 |
| Avoiding discrimination through causal reasoning N Kilbertus, M Rojas Carulla, G Parascandolo, M Hardt, D Janzing, ... Advances in neural information processing systems 30, 2017 | 726 | 2017 |
| Kernel-based conditional independence test and application in causal discovery K Zhang, J Peters, D Janzing, B Schölkopf arXiv preprint arXiv:1202.3775, 2012 | 717 | 2012 |
| On causal and anticausal learning B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij arXiv preprint arXiv:1206.6471, 2012 | 680 | 2012 |
| Causal discovery with continuous additive noise models J Peters, JM Mooij, D Janzing, B Schölkopf | 611 | 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 | 572 | 2016 |
| Information-geometric approach to inferring causal directions D Janzing, J Mooij, K Zhang, J Lemeire, J Zscheischler, P Daniušis, ... Artificial Intelligence 182, 1-31, 2012 | 364 | 2012 |
| Thermodynamic cost of reliability and low temperatures: Tightening Landauer's principle and the second law D Janzing, P Wocjan, R Zeier, R Geiss, T Beth International Journal of Theoretical Physics 39, 2717-2753, 2000 | 352 | 2000 |
| Feature relevance quantification in explainable AI: A causal problem D Janzing, L Minorics, P Blöbaum International Conference on artificial intelligence and statistics, 2907-2916, 2020 | 349 | 2020 |
| Causal inference using the algorithmic Markov condition D Janzing, B Schölkopf IEEE Transactions on Information Theory 56 (10), 5168-5194, 2010 | 336 | 2010 |
| Quantifying causal influences D Janzing, D Balduzzi, M Grosse-Wentrup, B Schölkopf | 269 | 2013 |
| Causal inference on time series using restricted structural equation models J Peters, D Janzing, B Schölkopf Advances in neural information processing systems 26, 2013 | 223 | 2013 |
| Inferring deterministic causal relations P Daniusis, D Janzing, J Mooij, J Zscheischler, B Steudel, K Zhang, ... arXiv preprint arXiv:1203.3475, 2012 | 218 | 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 | 198 | 2011 |
| Identifiability of causal graphs using functional models J Peters, J Mooij, D Janzing, B Schölkopf arXiv preprint arXiv:1202.3757, 2012 | 177 | 2012 |
| Regression by dependence minimization and its application to causal inference in additive noise models J Mooij, D Janzing, J Peters, B Schölkopf Proceedings of the 26th annual international conference on machine learning …, 2009 | 172 | 2009 |
| A quantum advantage for inferring causal structure K Ried, M Agnew, L Vermeyden, D Janzing, RW Spekkens, KJ Resch Nature Physics 11 (5), 414-420, 2015 | 158 | 2015 |
| Probabilistic latent variable models for distinguishing between cause and effect O Stegle, D Janzing, K Zhang, JM Mooij, B Schölkopf Advances in neural information processing systems 23, 2010 | 155 | 2010 |
| Causal consistency of structural equation models PK Rubenstein, S Weichwald, S Bongers, JM Mooij, D Janzing, ... arXiv preprint arXiv:1707.00819, 2017 | 122 | 2017 |
