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Sebastian Weichwald.

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I am an advocate of pragmatic causal modelling and aim at improving the applicability of statistical causal modelling and causal ML. We work on the conceptual foundations of causal model transformations and causal feature learning (UAI paper A & B), causal interpretation rules for common neuroimaging analyses (NeuroImage paper), pitfalls of causal discovery benchmarking (NeurIPS & arXiv paper), and won the Causality 4 Climate NeurIPS competition (PMLR paper).

I am Assistant Professor at the CoCaLa (Copenhagen Causality Lab, Department of Mathematical Sciences, University of Copenhagen), co-lead of the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI (P1), and chair of the young academy panel of the Danish Data Science Academy (DDSA). Before, I was a Postdoc here and a PhD student at the Max Planck Institute for Intelligent Systems and the ETH Zurich. I obtained my MSc in Computational Statistics and Machine Learning from University College London, funded by the German National Academic Foundation (Studienstiftung) and the German Academic Exchange Service (DAAD), and my BSc in Mathematics from the University of Tübingen.

Open Positions, Exchange, and Student Projects.

Open PhD positions are announced bi-annually (applications due April 1st or November 15th) and open Postdoc positions are announced annually (applications due November 15th) via our department calls.
You have an unconventional background or are not entirely sure the project exactly matches your interest? Please go ahead and apply in any case 😎 We can discuss any questions and all details during the application process. My apologies that I cannot respond to applications and individual questions emailed to me beforehand.

Please get in touch using the email subject prefix “[initiative]”, if you wish to propose an idea for working with me as an exchange researcher or plan on applying for your PhD or Postdoc funding individually, for example, through the DDSA fellowship programme.

If you are a student at the University of Copenhagen and would like to do your student/BSc/MSc project with me, please stop by my office or send me an email from your university email address using the email subject prefix “[UCPH]”.

News.

Jul 2023.

We released CausalDisco 🪩 – pip install CausalDisco to use simple SortnRegress baseline causal discovery algorithms and evaluate the R²- and Var-sortability of structure learning tasks.

Jun 2023.

Unfair Utilities and First Steps Towards Improving Them – in this new preprint we propose a change of focus and to assess the fairness of the utility while many fairness criteria constrain the policy or choice of predictors.

Apr 2023.

In our recent preprint, we show that “Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models” and describe an easily exploitable pattern, R²-sortability, that, in contrast to var-sortability, remains unchanged under data rescaling or standardization.

Mar 2023.

I have been appointed co-lead at P1 and am thrilled to join Ira Assent and Aasa Feragen as co-lead of the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI (P1) 🤓

Nov 2022.

Frederik Hytting Jørgensen is joining us as PhD student at the Copenhagen Causality Lab 🎉 (co-supervised by Jonas Peters)

Olds.