Copenhagen Causality Lab (CoCaLa)

🚧 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.

News

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

Members

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

Jeff Adams

Niels Richard Hansen

Shimeng Huang

Martin Emil Jakobsen

Snorre Jallbjørn

Steffen L. Lauritzen

Phillip Bredahl Mogensen

⭐ Mogensen & Markussen (2021) 🚀

Rikke Søndergaard Nielsen

Lasse Petersen

⭐ tba 🚀

⭐ Saengkyongam et al. (2021) 🚀

Sorawit Saengkyongam

⭐ Saengkyongam et al. (2021) 🚀

Nikolaj Theodor Thams

Sebastian Weichwald

⭐ Reisach et al. (2021) 🚀

Alumni

Rune Christiansen
Frederik Riis Mikkelsen
Søren Wengel Mogensen
Gherardo Varando

Articles

Preprints

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

Published articles (recent selection)

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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