| Anchor regression: Heterogeneous data meet causality D Rothenhäusler, N Meinshausen, P Bühlmann, J Peters Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2021 | 237 | 2021 |
| BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions D Rothenhäusler, C Heinze, J Peters, N Meinshausen advances in neural information processing systems 28, 2015 | 87 | 2015 |
| Causal Dantzig D Rothenhäusler, P Bühlmann, N Meinshausen The Annals of Statistics 47 (3), 1688-1722, 2019 | 48 | 2019 |
| Guilt in voting and public good games D Rothenhäusler, N Schweizer, N Szech European Economic Review 101, 664-681, 2018 | 41 | 2018 |
| Robust factor selection in early cell culture process development for the production of a biosimilar monoclonal antibody M Sokolov, J Ritscher, N MacKinnon, JM Bielser, D Brühlmann, ... Biotechnology progress 33 (1), 181-191, 2017 | 41 | 2017 |
| Causal inference in partially linear structural equation models D Rothenhäusler, J Ernest, P Bühlmann The Annals of Statistics 46 (6A), 2904-2938, 2018 | 29 | 2018 |
| Incremental causal effects D Rothenhäusler, B Yu arXiv preprint arXiv:1907.13258, 2019 | 19 | 2019 |
| The s-value: evaluating stability with respect to distributional shifts S Gupta, D Rothenhäusler Advances in Neural Information Processing Systems 36, 2024 | 17 | 2024 |
| Confidence intervals for maximin effects in inhomogeneous large-scale data D Rothenhäusler, N Meinshausen, P Bühlmann Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014, 255-277, 2016 | 16 | 2016 |
| Institutions, shared guilt, and moral transgression D Rothenhäusler, N Schweizer, N Szech CESifo Working Paper Series, 2015 | 14 | 2015 |
| Distributionally robust and generalizable inference D Rothenhäusler, P Bühlmann Statistical Science 38 (4), 527-542, 2023 | 13 | 2023 |
| Causal inference in partially linear structural equation models: identifiability and estimation J Ernest, D Rothenhäusler, P Bühlmann arXiv preprint arXiv:1607.05980, 2016 | 13 | 2016 |
| On the statistical role of inexact matching in observational studies K Guo, D Rothenhäusler Biometrika 110 (3), 631-644, 2023 | 10 | 2023 |
| Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty Y Jeong, D Rothenhäusler arXiv preprint arXiv:2202.11886, 2022 | 9 | 2022 |
| Tailored inference for finite populations: conditional validity and transfer across distributions Y Jin, D Rothenhäusler Biometrika 111 (1), 215-233, 2024 | 5 | 2024 |
| Bin Yu. Incremental causal effects D Rothenhäusler arXiv preprint arXiv:1907.13258, 2019 | 5 | 2019 |
| Learning under random distributional shifts KC Bansak, E Paulson, D Rothenhäusler International Conference on Artificial Intelligence and Statistics, 3943-3951, 2024 | 3 | 2024 |
| Diagnosing the role of observable distribution shift in scientific replications Y Jin, K Guo, D Rothenhäusler arXiv preprint arXiv:2309.01056, 2023 | 3 | 2023 |
| Causal aggregation: estimation and inference of causal effects by constraint-based data fusion JR Gimenez, D Rothenhäusler Journal of Machine Learning Research 23 (335), 1-60, 2022 | 2 | 2022 |
| Attribute-adaptive statistical inference for finite populations under distribution shift Y Jin, D Rothenhäusler arXiv preprint arXiv:2104.04565, 2021 | 2 | 2021 |
Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichGeverifieerd e-mailadres voor stat.math.ethz.ch