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
- fairness-aware machine learning, especially with a causal focus,
- heterogeneous treatment effects,
- counterfactual explanations,
- causal inference,
- applications in social sciences and econometrics.
Our research group holds a regular journal club focused on the latest advancements in causality and fairness as published in machine learning conferences and journals. This journal club is open for guests; please contact Ludwig Bothmann if you are interested in joining.
Members
Name | Position | |||
---|---|---|---|---|
Dr. Ludwig Bothmann | Lead | |||
Dr. Matthias Aßenmacher | PostDoc | |||
Susanne Dandl | PhD Student | |||
Gunnar König | PhD Student | |||
Lisa Wimmer | PhD Student |
Publications
- Bothmann L, Dandl S, Schomaker M (2023) Causal Fair Machine Learning via Rank-Preserving Interventional Distributions Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023), CEUR Workshop Proceedings.
link|pdf. - Weerts H, Pfisterer F, Feurer M, Eggensperger K, Bergman E, Awad N, Vanschoren J, Pechenizkiy M, Bischl B, Hutter F (2023) Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. arXiv:2303.08485 [cs.AI].
link|pdf. - Bothmann L, Peters K, Bischl B (2023) What Is Fairness? Philosophical Considerations and Implications For FairML. arXiv:2205.09622 [cs, stat].
link. - Urchs S, Thurner V, Aßenmacher M, Heumann C, Thiemichen S (2023) How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses 1st Workshop on Biased Data in Conversational Agents (co-located with ECML-PKDD 2023),
link|pdf. - Öztürk IT, Nedelchev R, Heumann C, Garces Arias E, Roger M, Bischl B, Aßenmacher M (2023) How Different Is Stereotypical Bias Across Languages? 3rd Workshop on Bias and Fairness in AI (co-located with ECML-PKDD 2023),
link|pdf. - 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