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

1. 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.
2. 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.
3. Bothmann L, Peters K, Bischl B (2023) What Is Fairness? Philosophical Considerations and Implications For FairML. arXiv:2205.09622 [cs, stat].
   link.
4. 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.
5. 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.
6. König G, Freiesleben T, Grosse-Wentrup M (2023) Improvement-focused Causal Recourse (ICR) 37th AAAI Conference,
7. Luther C, König G, Grosse-Wentrup M (2023) Efficient SAGE Estimation via Causal Structure Learning AISTATS,
8. Dandl S, Bender A, Hothorn T (2022) Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests. arXiv:2210.02836
   link.
9. Bothmann L (2022) Künstliche Intelligenz in der Strafverfolgung. In: In: Peters K (ed) Cyberkriminalität, LMU Munich, Munich.
   link .
10. 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.
11. Dandl S, Hothorn T, Seibold H, Sverdrup E, Wager S, Zeileis A (2022) What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
    link.
12. 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