Follow
Susanne Dandl
Title
Cited by
Cited by
Year
Multi-objective counterfactual explanations
S Dandl, C Molnar, M Binder, B Bischl
International Conference on Parallel Problem Solving from Nature, 448-469, 2020
3522020
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
269*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
132024
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
122021
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
72023
Multi-objective counterfactual fairness
S Dandl, F Pfisterer, B Bischl
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022
72022
counterfactuals: An R Package for Counterfactual Explanation Methods
S Dandl, A Hofheinz, M Binder, B Bischl, G Casalicchio
arXiv preprint arXiv:2304.06569, 2023
62023
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
42024
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
32024
Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations
S Dandl
lmu, 2023
22023
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
22023
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
L Bothmann, S Dandl, M Schomaker
arXiv preprint arXiv:2307.12797, 2023
22023
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
The system can't perform the operation now. Try again later.
Articles 1–18