Copenhagen Causality Lab (CoCaLa)

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.

Members

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

Jeff Adams

Alexander Mangulad Christgau

⭐ Christgau et al. (2022) 🚀

Niels Richard Hansen

Leonard Henckel

Shimeng Huang

Snorre Jallbjørn

Steffen L. Lauritzen

Phillip Bredahl Mogensen

⭐ Mogensen & Markussen (2021) 🚀

Rikke Søndergaard Nielsen

⭐ Thams et al. (2021) 🚀

⭐ Saengkyongam et al. (2021) 🚀

Sorawit Saengkyongam

⭐ Saengkyongam et al. (2021) 🚀

Nikolaj Theodor Thams

⭐ Thams* et al. (2022) 🚀

Sebastian Weichwald

⭐ Reisach et al. (2021) 🚀

Present visitors

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Future visitors

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Alumni

Rune Christiansen
Martin Emil Jakobsen
Frederik Riis Mikkelsen
Søren Wengel Mogensen
Lasse Petersen
Gherardo Varando

Past visitors

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Articles

Preprints

2022 – “Exploiting independent instruments: Identification and distribution generalization”
S. Saengkyongam, L. Henckel, N. Pfister, & J. Peters
arXiv Preprint arXiv:2202.01864.
arXiv | bib
2022 – “Identifying causal effects using instrumental time series: Nuisance IV and correcting for the past”
N. Thams, R. Søndergaard, S. Weichwald, & J. Peters
arXiv Preprint arXiv:2203.06056.
bib
2022 – “Identifiability of sparse causal effects using instrumental variables”
N. Pfister, & J. Peters
arXiv Preprint arXiv:2203.09380.
arXiv | bib
2022 – “Nonparametric conditional local independence testing”
A. M. Christgau, L. Petersen, & N. R. Hansen
arXiv Preprint arXiv:2203.13559.
arXiv | bib
2022 – “Supervised learning and model analysis with compositional data”
Shimeng Huang, E. Ailer, N. Kilbertus, & N. Pfister
arXiv Preprint arXiv:2205.07271.
arXiv | bib
2022 – “Supervised learning and model analysis with compositional data”
S. Huang, E. Ailer, N. Kilbertus, & N. Pfister
arXiv Preprint arXiv:2205.07271.
arXiv | bib
2022 – “Evaluating robustness to dataset shift via parametric robustness sets”
N. Thams*, M. Oberst*, & D. Sontag
arXiv Preprint arXiv:2205.15947.
arXiv | bib
2022 – “Invariant ancestry search”
P. Mogensen, N. Thams, & J. Peters
Proceedings of the 39th International Conference on Machine Learning, To appear.
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! Causal Discovery Benchmarks May Be Easy To Game”
A. G. Reisach, C. Seiler, & S. Weichwald
arXiv Preprint arXiv:2102.13647 (Accepted at NeurIPS 2021).
arXiv | bib | url
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”
Sorawit 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 – “Local independence testing for point processes”
N. Thams, & N. R. Hansen
arXiv Preprint arXiv:2110.12709.
arXiv | bib
2021 – “The three major axes of terrestrial ecosystem function”
M. Migliavacca, T. Musavi, M. D. Mahecha, J. A. Nelson, J. Knauer, D. D. Baldocchi, O. Perez-Priego, R. Christiansen, J. Peters, K. Anderson, M. Bahn, T. A. Black, P. D. Blanken, D. Bonal, N. Buchmann, S. Caldararu, A. Carrara, N. Carvalhais, A. Cescatti, … M. Reichstein
Nature (Accepted), x, x–x.
bib | url
2021 – “Distributional robustness of k-class estimators and the PULSE”
M. Jakobsen, & J. Peters
The Econometrics Journal (Accepted).
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).
bib

Published articles (recent selection)

2022 – “Causal models for dynamical systems”
J. Peters, S. Bauer, & N. Pfister
In Probabilistic and causal inference: The works of judea pearl (pp. 671–690).
arXiv | bib | url
2021 – “Causal discovery in heavy-tailed models”
N. Gnecco, N. Meinshausen, J. Peters, & S. Engelke
Annals of Statistics, 49(3), 1755–1778.
arXiv | bib
2021 – “A causal framework for distribution generalization”
R. Christiansen, N. Pfister, M. Jakobsen, N. Gnecco, & J. Peters
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1.
arXiv | bib | url
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
2021 – “Stabilizing variable selection and regression”
N. Pfister, E. G. Williams, J. Peters, R. Aebersold, & P. Bühlmann
The Annals of Applied Statistics, 15(3), 1220–1246.
arXiv | bib | url
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