| Regularizing double machine learning in partially linear endogenous models C Emmenegger, P Bühlmann Electronic Journal of Statistics 15 (2), 6461-6543, 2021 | 6 | 2021 |
| Treatment effect estimation from observational network data using augmented inverse probability weighting and machine learning C Emmenegger, ML Spohn, T Elmer, P Bühlmann arXiv preprint arXiv:2206.14591, 2022 | 3 | 2022 |
| Confidence and uncertainty assessment for distributional random forests J Näf, C Emmenegger, P Bühlmann, N Meinshausen Journal of Machine Learning Research 24 (366), 1-77, 2023 | 2 | 2023 |
| Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements C Emmenegger, P Bühlmann arXiv preprint arXiv:2108.13657, 2021 | 2* | 2021 |
| dmlalg: Double Machine Learning Algorithms C Emmenegger R package, URL https://cran.r-project.org/package=dmlalg, 2021 | 2 | 2021 |
| TSCI: two stage curvature identification for causal inference with invalid instruments D Carl, C Emmenegger, P Bühlmann, Z Guo arXiv preprint arXiv:2304.00513, 2023 | | 2023 |
| Statistical Machine Learning for Complex Data C Emmenegger ETH Zurich, 2023 | | 2023 |
| Double machine learning methods: Beyond independence C Emmenegger, PL Bühlmann, ML Spohn Re-thinking High-dimensional Mathematical Statistics (Workshop Report 2022 …, 2022 | | 2022 |
| Regularized Double Machine Learning in Partially Linear Models with Unobserved Confounding P Emmenegger, Corinne and Bühlmann Proceedings 63rd ISI World Statistics Congress 11, 16, 2021 | | 2021 |
Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichVerified email at stat.math.ethz.ch
