Invariant causal prediction for nonlinear models C Heinze-Deml, J Peters, N Meinshausen Journal of Causal Inference 6 (2), 20170016, 2018 | 277 | 2018 |
Causal structure learning C Heinze-Deml, MH Maathuis, N Meinshausen Annual Review of Statistics and Its Application 5, 2017 | 233 | 2017 |
Conditional variance penalties and domain shift robustness C Heinze-Deml, N Meinshausen Machine Learning 110 (2), 303-348, 2021 | 166 | 2021 |
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions D Rothenhäusler, C Heinze, J Peters, N Meinshausen Advances in Neural Information Processing Systems (NIPS) 29, 2015, 2015 | 81 | 2015 |
Random projections for large-scale regression GA Thanei, C Heinze, N Meinshausen Big and Complex Data Analysis: Methodologies and Applications, 51-68, 2017 | 57 | 2017 |
Predicting causal relationships from biological data: Applying automated causal discovery on mass cytometry data of human immune cells S Triantafillou, V Lagani, C Heinze-Deml, A Schmidt, J Tegner, ... Scientific reports 7 (1), 12724, 2017 | 48 | 2017 |
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections C Heinze, B McWilliams, N Meinshausen Proceedings of the 19th International Conference on Artificial Intelligence …, 2016 | 48 | 2016 |
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness F Yang, Z Wang, C Heinze-Deml Advances in Neural Information Processing Systems (NeurIPS) 33, 2019, 2019 | 41 | 2019 |
Active invariant causal prediction: Experiment selection through stability JL Gamella, C Heinze-Deml Advances in Neural Information Processing Systems (NeurIPS) 34, 2020, 2020 | 38 | 2020 |
Loco: Distributing ridge regression with random projections C Heinze, B McWilliams, N Meinshausen, G Krummenacher arXiv preprint arXiv:1406.3469, 2014 | 35* | 2014 |
Think before you act: A simple baseline for compositional generalization C Heinze-Deml, D Bouchacourt arXiv preprint arXiv:2009.13962, 2020 | 15 | 2020 |
Preserving privacy between features in distributed estimation C Heinze‐Deml, B McWilliams, N Meinshausen stat 7 (1), e189, 2018 | 11 | 2018 |
CompareCausalNetworks: interface to diverse estimation methods of causal networks C Heinze-Deml, N Meinshausen R package, 2017 | 9* | 2017 |
Latent linear adjustment autoencoders v1. 0: A novel method for estimating and emulating dynamic precipitation at high resolution C Heinze-Deml, S Sippel, AG Pendergrass, F Lehner, N Meinshausen Geoscientific Model Development Discussions 2020, 1-39, 2020 | 8 | 2020 |
Perturbations and Causality in Gaussian Latent Variable Models A Taeb, JL Gamella, C Heinze-Deml, P Bühlmann arXiv preprint arXiv:2101.06950v3, 2021 | 5 | 2021 |
Considerations for distribution shift robustness in health A Blaas, A Miller, L Zappella, JH Jacobsen, C Heinze-Deml ICLR 2023 Workshop on Trustworthy Machine Learning for Healthcare, 2023 | 3 | 2023 |
Characterization and greedy learning of Gaussian structural causal models under unknown interventions JL Gamella, A Taeb, C Heinze-Deml, P Bühlmann arXiv preprint arXiv:2211.14897, 2022 | 3 | 2022 |
Transfer learning for estimating dynamic precipitation across different climate models J Kuettel, S Sippel, C Heinze-Deml, R Knutti, N Meinshausen EGU22, 2022 | | 2022 |
Package ‘CondIndTests’ C Heinze-Deml, J Peters, AMS Munk, MC Heinze-Deml, T LazyData | | 2019 |
Computational Causality and Learning from Partitioned Data C Heinze-Deml ETH Zurich, 2018 | | 2018 |