| Regularizing towards causal invariance: Linear models with proxies M Oberst, N Thams, J Peters, D Sontag International Conference on Machine Learning, 8260-8270, 2021 | 18 | 2021 |
| Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values S Weichwald, ME Jakobsen, PB Mogensen, L Petersen, N Thams, ... NeurIPS 2019 Competition and Demonstration Track, 27-36, 2020 | 17 | 2020 |
| Statistical testing under distributional shifts N Thams, S Saengkyongam, N Pfister, J Peters arXiv preprint arXiv:2105.10821, 2021 | 6 | 2021 |
| Invariant policy learning: A causal perspective S Saengkyongam, N Thams, J Peters, N Pfister IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 | 4 | 2023 |
| Evaluating robustness to dataset shift via parametric robustness sets N Thams, M Oberst, D Sontag arXiv preprint arXiv:2205.15947, 2022 | 3 | 2022 |
| Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past N Thams, R Søndergaard, S Weichwald, J Peters arXiv preprint arXiv:2203.06056, 2022 | 2 | 2022 |
| Causal structure learning in multivariate point processes N Thams Master’s 434, 2019 | 2 | 2019 |
| Invariant Ancestry Search PB Mogensen, N Thams, J Peters International Conference on Machine Learning, 15832-15857, 2022 | 1 | 2022 |
| Local Independence Testing for Point Processes N Thams, NR Hansen arXiv preprint arXiv:2110.12709, 2021 | 1 | 2021 |
| Evaluating Robustness to Dataset Shift via Parametric Robustness Sets M Oberst, N Thams, D Sontag ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 0 | | |
