| Quantifying causal contribution via structure preserving interventions D Janzing, P Blöbaum, L Minorics, P Faller arXiv preprint arXiv:2007.00714, 2020 | 8 | 2020 |
| Forcing interpretability for deep neural networks through rule-based regularization N Burkart, M Huber, P Faller 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 7 | 2019 |
| Causal forecasting: generalization bounds for autoregressive models LC Vankadara, PM Faller, M Hardt, L Minorics, D Ghoshdastidar, ... Uncertainty in Artificial Intelligence, 2002-2012, 2022 | 5 | 2022 |
| Batch-wise regularization of deep neural networks for interpretability N Burkart, PM Faller, E Peinsipp, MF Huber 2020 IEEE International Conference on Multisensor Fusion and Integration for …, 2020 | 5 | 2020 |
| Self-compatibility: Evaluating causal discovery without ground truth PM Faller, LC Vankadara, AA Mastakouri, F Locatello, D Janzing International Conference on Artificial Intelligence and Statistics, 4132-4140, 2024 | 2 | 2024 |
| Quantifying intrinsic causal contributions via structure preserving interventions D Janzing, P Blöbaum, AA Mastakouri, PM Faller, L Minorics, ... International Conference on Artificial Intelligence and Statistics, 2188-2196, 2024 | 2 | 2024 |
| Reinterpreting causal discovery as the task of predicting unobserved joint statistics D Janzing, PM Faller, LC Vankadara arXiv preprint arXiv:2305.06894, 2023 | 1 | 2023 |
| Causal Forecasting: Generalization Bounds for Autoregressive Models L Chennuru Vankadara, PM Faller, M Hardt, L Minorics, D Ghoshdastidar, ... arXiv e-prints, arXiv: 2111.09831, 2021 | | 2021 |
| Causal Forecasting: Generalization Bounds for Autoregressive Models-Supplementary Material LC Vankadara, PM Faller, M Hardt, L Minorics, D Ghoshdastidar, ... | | |
