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:

Members

Name       Position
Dr. Ludwig Bothmann       Lead
Susanne Dandl       PhD Student
Gunnar König       PhD Student
Lisa Wimmer       PhD Student

Publications

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