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 040

Phone: +49 89 2180 2763

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

Research Interests

• Interpretable machine learning
• Causal personalized treatment effect estimation with tree- and forest-based models.

You can find me on

• GitHub
• ResearchGate
• Google Scholar

References

1. Dandl S, Pfisterer F, Bischl B (2022) Multi-Objective Counterfactual Fairness To appear in 2022 Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion), ACM, Boston, USA.
2. 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
   link.
3. 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. https://doi.org/10.1007/978-3-031-04083-2_4.
4. 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.
   link.
5. 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.
   link | video.