🚧 we just launched this website, so there may be some changes and/or glitches here and there 👷

We do research on causality, statistical modelling, and data analysis.

You can find us at the Department of Mathematical Sciences, University of Copenhagen and converse with us on matrix – just join #lobby:ucph.ems.host via the open network for secure, decentralized, cross-institutional communication.

Please reach out to us when you are interested in joining or working with us! Positions are announced via the department’s calls. The annual PhD calls have deadlines in April and November. The annual postdoc call opens in October with a deadline in November.

- July 2021. The Learning By Doing – NeurIPS 2021 Competition launched early July 🚀

⭐ [current favourite paper that the lab member co-authored] 🚀

Jeff Adams

Steffen L. Lauritzen

Rikke Søndergaard Nielsen

- Rune Christiansen
- Frederik Riis Mikkelsen
- Søren Wengel Mogensen
- Gherardo Varando

- 2021 – “
**Stabilizing variable selection and regression**”N. Pfister, E. G. Williams, J. Peters, R. Aebersold, & P. Bühlmann*Annals of Applied Statistics (Accepted)*.

arXiv | bib - 2021 – “
**Causal discovery in heavy-tailed models**”N. Gnecco, N. Meinshausen, J. Peters, & S. Engelke*Annals of Statistics (Accepted)*.

arXiv | bib - 2021 – “
**Foundations of structural causal models with cycles and latent variables**”S. Bongers, P. Forre, J. Peters, & J. M. Mooij*Annals of Statistics (Accepted)*.

arXiv | bib - 2021 – “
**Conditional independence testing in Hilbert spaces with applications to functional data analysis**”A. R. Lundborg, R. D. Shah, & J. Peters*ArXiv e-Prints (2101.07108)*.

arXiv | bib - 2021 – “
**Beware of the Simulated DAG! Varsortability in Additive Noise Models**”A. G. Reisach, C. Seiler, & S. Weichwald*arXiv Preprint arXiv:2102.13647*.

arXiv | bib - 2021 – “
**Compositional Abstraction Error and a Category of Causal Models**”E. F. Rischel, & S. Weichwald*arXiv Preprint arXiv:2103.15758 (Accepted at UAI 2021)*.

arXiv | bib | url - 2021 – “
**Statistical testing under distributional shifts**”N. Thams, S. Saengkyongam, N. Pfister, & J. Peters*arXiv Preprint arXiv:2105.10821*.

arXiv | bib - 2021 – “
**Invariant policy learning: A causal perspective**”S. Saengkyongam, N. Thams, J. Peters, & N. Pfister*arXiv Preprint arXiv:2106.00808*.

arXiv | bib - 2021 – “
**’Too many, too improbable’test statistics: A general method for testing joint hypotheses and controlling the k-FWER**”P. B. Mogensen, & B. Markussen*arXiv Preprint arXiv:2108.04731*.

arXiv | bib - 2021 – “
**The difficult task of distribution generalization in nonlinear models**”R. Christiansen, N. Pfister, M. Jakobsen, N. Gnecco, & J. Peters*IEEE Transaction of Pattern Analysis and Machine Intelligence (Accepted)*.

arXiv | bib - 2020 – “
**Distributional robustness of k-class estimators and the PULSE**”M. Jakobsen, & J. Peters*ArXiv e-Prints (2005.03353)*.

arXiv | bib - 2020 – “
**Towards causal inference for spatio-temporal data: Conflict and forest loss in Colombia**”R. Christiansen, M. Baumann, T. Kümmerle, M. Mahecha, & J. Peters*ArXiv e-Prints (2005.08639)*.

arXiv | bib

- 2021 – “
**Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness**”S. Weichwald, & J. Peters*Journal of Cognitive Neuroscience*,*33*(2), 226–247.

arXiv | bib | url - 2021 – “
**Anchor regression: Heterogeneous data meets causality**”D. Rothenhäusler, P. Bühlmann, N. Meinshausen, & J. Peters*Journal of Royal Statistical Society, Series B*,*83*(2), 215–246.

arXiv | bib - 2021 – “
**Regularizing towards causal invariance: Linear models with proxies**”M. Oberst, N. Thams, J. Peters, & D. Sontag*Proceedings of the 38th International Conference on Machine Learning (ICML)*.

arXiv | bib - 2020 – “
**The hardness of conditional independence testing and the generalised covariance measure**”R. Shah, & J. Peters*Annals of Statistics*,*48*(3), 1514–1538.

arXiv | bib | url - 2020 – “
**Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values**”S. Weichwald, M. E. Jakobsen, P. B. Mogensen, L. Petersen, N. Thams, & G. Varando*Proceedings of the NeurIPS 2019 Competition and Demonstration Track, Proceedings of Machine Learning Research*,*123*, 27–36.

arXiv | bib | url - 2019 – “
**Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise**”N. Pfister, S. Weichwald, P. Bühlmann, & B. Schölkopf*Journal of Machine Learning Research*,*20*(147), 1–50.

arXiv | bib | url - 2019 – “
**Learning stable and predictive structures in kinetic systems**”N. Pfister, S. Bauer, & J. Peters*Proceedings of the National Academy of Sciences*,*116*(51), 25405–25411.

arXiv | bib | url

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