| 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 | 300 | 2020 |
| Cause-effect inference by comparing regression errors P Blöbaum, D Janzing, T Washio, S Shimizu, B Schölkopf International Conference on Artificial Intelligence and Statistics, 900-909, 2018 | 81 | 2018 |
| Causal structure-based root cause analysis of outliers K Budhathoki, L Minorics, P Blöbaum, D Janzing International Conference on Machine Learning, 2357-2369, 2022 | 42* | 2022 |
| Why did the distribution change? K Budhathoki, D Janzing, P Blöbaum, H Ng International Conference on Artificial Intelligence and Statistics, 1666-1674, 2021 | 32 | 2021 |
| DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models P Blöbaum, P Götz, K Budhathoki, AA Mastakouri, D Janzing arXiv preprint arXiv:2206.06821, 2022 | 28 | 2022 |
| On Measuring Causal Contributions via do-interventions Y Jung, S Kasiviswanathan, J Tian, D Janzing, P Blöbaum, E Bareinboim International Conference on Machine Learning, 10476-10501, 2022 | 18 | 2022 |
| Analysis of cause-effect inference by comparing regression errors P Blöbaum, D Janzing, T Washio, S Shimizu, B Schölkopf PeerJ Computer Science 5, e169, 2019 | 18 | 2019 |
| Sequential kernelized independence testing A Podkopaev, P Blöbaum, S Kasiviswanathan, A Ramdas International Conference on Machine Learning, 27957-27993, 2023 | 11 | 2023 |
| Interventional and Counterfactual Inference with Diffusion Models P Chao, P Blöbaum, SP Kasiviswanathan arXiv preprint arXiv:2302.00860, 2023 | 11 | 2023 |
| Quantifying intrinsic causal contributions via structure preserving interventions D Janzing, P Blöbaum, L Minorics, P Faller, A Mastakouri arXiv preprint arXiv:2007.00714, 2020 | 10 | 2020 |
| Estimation of interventional effects of features on prediction P Blöbaum, S Shimizu IEEE 27th International Workshop on Machine Learning for Signal Processing …, 2017 | 10 | 2017 |
| Error asymmetry in causal and anticausal regression P Blöbaum, T Washio, S Shimizu Behaviormetrika, 1-22, 2017 | 9 | 2017 |
| Unsupervised Dimensionality Reduction for Transfer Learning P Blöbaum, A Schulz, B Hammer 23rd European Symposium on Artificial Neural Networks, Computational …, 2015 | 5 | 2015 |
| Discriminative and generative models in causal and anticausal settings P Blöbaum, S Shimizu, T Washio Advanced Methodologies for Bayesian Networks: Second International Workshop …, 2015 | 4 | 2015 |
| Thompson Sampling with Diffusion Generative Prior YG Hsieh, SP Kasiviswanathan, B Kveton, P Blöbaum International Conference on Machine Learning, 13434-13468, 2023 | 3 | 2023 |
| Recent advances in semi-parametric methods for causal discovery S Shimizu, P Blöbaum Direction Dependence in Statistical Modeling: Methods of Analysis, 111-130, 2020 | 3 | 2020 |
| Toward Falsifying Causal Graphs Using a Permutation-Based Test E Eulig, AA Mastakouri, P Blöbaum, M Hardt, D Janzing arXiv preprint arXiv:2305.09565, 2023 | 2 | 2023 |
| Manifold restricted interventional shapley values MF Taufiq, P Blöbaum, L Minorics International Conference on Artificial Intelligence and Statistics, 5079-5106, 2023 | 2 | 2023 |
| Beyond Single-Feature Importance with ICECREAM M Oesterle, P Blöbaum, AA Mastakouri, E Kirschbaum arXiv preprint arXiv:2307.09779, 2023 | 1 | 2023 |
| Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies L Minorics, C Turkmen, D Kernert, P Blöbaum, L Callot, D Janzing International Conference on Artificial Intelligence and Statistics, 10534-10554, 2022 | 1 | 2022 |
