Causal and Fair Machine Learning
This group focuses on methodological and applied research in the context of causal questions in machine learning and on fairness in machine learning. Topics include:
- Causal Inference
- Fair Machine Learning
Members
Name | Position | |||
---|---|---|---|---|
Dr. Ludwig Bothmann | Lead | |||
Susanne Dandl | PhD Student | |||
Gunnar König | PhD Student | |||
Lisa Wimmer | PhD Student |
Publications
- Bothmann L, Peters K, Bischl B (2023) What Is Fairness? Philosophical Considerations and Implications For FairML. arXiv:2205.09622 [cs, stat].
link. - König G, Freiesleben T, Grosse-Wentrup M (2023) Improvement-focused Causal Recourse (ICR) 37th AAAI Conference,
- Luther C, König G, Grosse-Wentrup M (2023) Efficient SAGE Estimation via Causal Structure Learning AISTATS,
- Dandl S, Bender A, Hothorn T (2022) Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests. arXiv:2210.02836
link. - Bothmann L (2022) Künstliche Intelligenz in der Strafverfolgung. In: In: Peters K (ed) Cyberkriminalität, LMU Munich, Munich.
link . - Dandl S, Pfisterer F, Bischl B (2022) Multi-Objective Counterfactual Fairness Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 328–331. Association for Computing Machinery, New York, NY, USA.
link. - Dandl S, Hothorn T, Seibold H, Sverdrup E, Wager S, Zeileis A (2022) What Makes Forest-Based Heterogeneous Treatment Effect
Estimators Work?
link. - König G, Grosse-Wentrup M (2019) A Causal Perspective on Challenges for AI in Precision Medicine.
link.
Contact
Feel free to contact us if you are looking for collaborations:
ludwig.bothmann [at] stat.uni-muenchen.de