Professor of Mathematical Statistics
About me
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I am professor of mathematical statistics at the Korteweg-De Vries Institute of the University of Amsterdam, the Netherlands. The research focus of my group is on causality, and spans a broad spectrum from causal modeling, discovery, and inference to applications in biology, medicine, fairness and business analytics. We combine mathematical modeling, statistical and algorithmic aspects. |
Lecture notes
The lecture notes A Mathematical Introduction to Causality for the MasterMath course on Causality that I taught together with Patrick Forré are available for public dissemination. We might make a book of this at some point, so any feedback is welcome!
News
- Feb 2024: The Dutch newspaper NRC published an interview (in Dutch, behind a paywall) with me about my research.
- Feb 2024: The third Amsterdam Causality Meeting will take place on February 29th.
- Nov 2023: The second Amsterdam Causality Meeting will take place on November 23rd.
- Sep 2023: We are organizing a new seminar, the Amsterdam Causality Meeting. First edition will take place on October 9th!
- Sep 2023: Sourbh Bhadane joined my group as postdoc.
- Aug 2023: Best paper award for our UAI paper Establishing Markov equivalence in cyclic directed graphs. Embarassingly, after publication we discovered a serious mistake, which we luckily managed to fix. Here is the corrected version.
- Jun 2023: I gave a plenary talk on Causality and fairness at AIM2023, the second workshop on AI and Mathematics.
- Jun 2023: My inaugural lecture was published in Nieuw Archief voor Wiskunde.
- May 2023: Thijs van Ommen and I organized the SIKS Course on Causal Inference 2023.
- Apr 2023: Stéphanie van der Pas, Richard Post and I organized the YES Causal Inference workshop.
- Mar 2023: This work by my former PhD student Tineke Blom, Causality and Independence in Perfectly Adapted Dynamical Systems, has drastically changed my view on causality in complex systems. Bottom line: these systems can show a causal semantics that is more complex than we are used to in DAGs.
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