| 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 | 30 | 2020 |
| Regularizing towards causal invariance: Linear models with proxies M Oberst, N Thams, J Peters, D Sontag International Conference on Machine Learning, 8260-8270, 2021 | 24 | 2021 |
| Invariant policy learning: A causal perspective S Saengkyongam, N Thams, J Peters, N Pfister IEEE transactions on pattern analysis and machine intelligence, 2023 | 18 | 2023 |
| Statistical testing under distributional shifts N Thams, S Saengkyongam, N Pfister, J Peters Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023 | 8 | 2023 |
| 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 | 6 | 2022 |
| Evaluating robustness to dataset shift via parametric robustness sets N Thams, M Oberst, D Sontag Advances in Neural Information Processing Systems 35, 16877-16889, 2022 | 5 | 2022 |
| Invariant ancestry search PB Mogensen, N Thams, J Peters International Conference on Machine Learning, 15832-15857, 2022 | 3 | 2022 |
| Causal structure learning in multivariate point processes N Thams Master’s 434, 2019 | 2 | 2019 |
| Local Independence Testing for Point Processes N Thams, NR Hansen IEEE Transactions on Neural Networks and Learning Systems, 2023 | 1 | 2023 |
| Causality and distribution shift NTB Thams Department of Mathematical Sciences, University of Copenhagen, 2022 | 1 | 2022 |
| Evaluating Robustness to Dataset Shift via Parametric Robustness Sets M Oberst, N Thams, D Sontag ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 2022 | | 2022 |
