- 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.
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- 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).
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- 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).
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