Christoph Molnar


Since october 2017 I am a PhD student at the working group for Computational Statistics at the Ludwig-Maximilians-University Munich, doing my research on Interpretable Machine Learning.

I obtained a Bachelor's Degree (B.Sc.) and Master's Degree (M.Sc.) in Statistics from the Ludwig-Maximilians-University Munich.


Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 139

christoph.molnar [at]

Research Interests

You Can Find me on


  1. Dandl S, Molnar C, Binder M, Bischl B (2020) Multi-Objective Counterfactual Explanations. arXiv preprint arXiv:2004.11165, accepted for PPSN 2020.
  2. Scholbeck CA, Molnar C, Heumann C, Bischl B, Casalicchio G (2020) Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 205–216. Springer International Publishing, Cham.
    DOI: 10.1007/978-3-030-43823-4_18 | PDF: arXiv:1904.03959
  3. Molnar C, König G, Herbinger J et al. (2020) Pitfalls to Avoid when Interpreting Machine Learning Models. arXiv preprint arXiv:2007.04131.
    LINK: arXiv:2007.04131
  4. Molnar C, König G, Bischl B, Casalicchio G (2020) Model-agnostic Feature Importance and Effects with Dependent Features–A Conditional Subgroup Approach. arXiv preprint arXiv:2006.04628.
    LINK: arXiv:2006.04628
  5. König G, Molnar C, Bischl B, Grosse-Wentrup M (2020) Relative Feature Importance. arXiv preprint arXiv:2007.08283.
  6. Casalicchio G, Molnar C, Bischl B (2019) Visualizing the Feature Importance for Black Box Models. In: In: Berlingerio M , In: Bonchi F , In: Gärtner T , In: Hurley N , In: Ifrim G (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018, pp. 655–670. Springer International Publishing, Cham.
    DOI: 10.1007/978-3-030-10925-7_40 | PDF: arXiv:1804.06620
  7. Molnar C (2019) Interpretable Machine Learning.
  8. Molnar C, Casalicchio G, Bischl B (2019) Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 193–204. Springer.
  9. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786. 10.21105/joss.00786.