| Multi-objective counterfactual explanations S Dandl, C Molnar, M Binder, B Bischl International Conference on Parallel Problem Solving from Nature, 448-469, 2020 | 325 | 2020 |
| General pitfalls of model-agnostic interpretation methods for machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... xxAI - Beyond Explainable AI. xxAI 2020. Lecture Notes in Computer Science, 2022 | 246* | 2022 |
| What makes forest-based heterogeneous treatment effect estimators work? S Dandl, C Haslinger, T Hothorn, H Seibold, E Sverdrup, S Wager, ... The Annals of Applied Statistics 18 (1), 506-528, 2024 | 11 | 2024 |
| mcboost: Multi-Calibration Boosting for R F Pfisterer, C Kern, S Dandl, M Sun, MP Kim, B Bischl Journal of Open Source Software 6 (64), 3453, 2021 | 10 | 2021 |
| Interpretable regional descriptors: Hyperbox-based local explanations S Dandl, G Casalicchio, B Bischl, L Bothmann Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 7 | 2023 |
| Multi-objective counterfactual fairness S Dandl, F Pfisterer, B Bischl Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022 | 7 | 2022 |
| counterfactuals: An R Package for Counterfactual Explanation Methods S Dandl, A Hofheinz, M Binder, B Bischl, G Casalicchio arXiv preprint arXiv:2304.06569, 2023 | 5 | 2023 |
| Heterogeneous treatment effect estimation for observational data using model-based forests S Dandl, A Bender, T Hothorn Statistical Methods in Medical Research 33 (3), 392-413, 2024 | 3 | 2024 |
| Causal Fair Machine Learning via Rank-Preserving Interventional Distributions L Bothmann, S Dandl, M Schomaker arXiv preprint arXiv:2307.12797, 2023 | 2 | 2023 |
| Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations S Dandl lmu, 2023 | 1 | 2023 |
| Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation J Herbinger, S Dandl, FK Ewald, S Loibl, G Casalicchio European Conference on Artificial Intelligence, 232-249, 2023 | 1 | 2023 |
| CountARFactuals–Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests S Dandl, K Blesch, T Freiesleben, G König, J Kapar, B Bischl, MN Wright World Conference on Explainable Artificial Intelligence, 85-107, 2024 | | 2024 |
| Causal Fair Machine Learning L Bothmann, K Peters, S Dandl, M Schomaker, B Bischl Statistical Computing 2024, 10, 2024 | | 2024 |
| mlr3summary: Concise and interpretable summaries for machine learning models S Dandl, M Becker, B Bischl, G Casalicchio, L Bothmann arXiv preprint arXiv:2404.16899, 2024 | | 2024 |
| Package ‘mlr3summary’ S Dandl, M Becker, B Bischl, G Casalicchio, L Bothmann, Dandl | | 2024 |
| Model Interpretation S Dandl, P Biecek, G Casalicchio, MN Wright Applied Machine Learning Using mlr3 in R, 259-282, 2024 | | 2024 |
| Package 'counterfactuals' S Dandl, A Hofheinz, M Binder, G Casalicchio https://doi.org/10.32614/CRAN.package.counterfactuals, 2023 | | 2023 |
| Bachelorarbeit: Empirischer Vergleich ordinaler Regressionsmodelle S Dandl | | 2016 |
