| On the identifiability and estimation of causal location-scale noise models A Immer, C Schultheiss, JE Vogt, B Schölkopf, P Bühlmann, A Marx International Conference on Machine Learning, 14316-14332, 2023 | 33 | 2023 |
| Multicarving for high-dimensional post-selection inference C Schultheiss, C Renaux, P Bühlmann Electronic Journal of Statistics 15 (1), 1695-1742, 2021 | 17 | 2021 |
| Ancestor regression in linear structural equation models C Schultheiss, P Bühlmann Biometrika 110 (4), 1117-1124, 2023 | 6 | 2023 |
| Higher-order least squares: assessing partial goodness of fit of linear causal models C Schultheiss, P Bühlmann, M Yuan Journal of the American Statistical Association, 2023 | 5 | 2023 |
| On the pitfalls of Gaussian likelihood scoring for causal discovery C Schultheiss, P Bühlmann Journal of Causal Inference 11 (1), 20220068, 2023 | 4 | 2023 |
| The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning N Langer, M Weber, B Hebling Vieira, D Strzelczyk, L Wolf, A Pedroni, ... bioRxiv, 2022 | 2 | 2022 |
| Ancestor regression in structural vector autoregressive models C Schultheiss, P Bühlmann arXiv preprint arXiv:2403.03778, 2024 | | 2024 |
| Assessing the overall and partial causal well-specification of nonlinear additive noise models C Schultheiss, P Bühlmann Journal of Machine Learning Research 25 (159), 1-41, 2024 | | 2024 |
| Automating clinical assessments of memory deficits: Deep Learning based scoring of the Rey-Osterrieth Complex Figure N Langer, M Weber, B Hebling Vieira, D Strzelczyk, L Wolf, A Pedroni, ... bioRxiv, 2022.06. 15.496291, 2022 | | 2022 |
