Susanne Dandl

About

I started as a PhD student at the Chair of Statistical Learning and Data Science of the Department of Statistics at the LMU in October 2019. My main research focus is on Causality Concepts in Machine Learning.

I obtained a Bachelor's Degree (B.Sc.) and Master's Degree (M.Sc.) in Statistics from the LMU.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 139

Phone: +49 89 2180 6408

Susanne.Dandl [at] stat.uni-muenchen.de

Research Interests

You can find me on

References

  1. 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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  2. 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.
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  3. Dandl S, Hothorn T, Seibold H, Sverdrup E, Wager S, Zeileis A (2022) What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work? arXiv:2206.10323
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  4. Molnar C, König G, Herbinger J et al. (2022) General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models xxAI - Beyond Explainable AI, pp. 39–68. Springer International Publishing.
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  5. Pfisterer F, Kern C, Dandl S, Sun M, Kim MP, Bischl B (2021) mcboost: Multi-Calibration Boosting for R. Journal of Open Source Software 6, 3453.
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  6. Dandl S, Molnar C, Binder M, Bischl B (2020) Multi-Objective Counterfactual Explanations. In: In: Bäck T , In: Preuss M , In: Deutz A et al. (eds) Parallel Problem Solving from Nature – PPSN XVI, pp. 448–469. Springer International Publishing, Cham.
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