- 2022 – “Evaluating robustness to dataset shift via parametric robustness sets”
N. Thams*, M. Oberst*, & D. Sontag
Advances in Neural Information Processing Systems.
arXiv | bib | url
- 2022 – “A causal framework for distribution generalization”
R. Christiansen, N. Pfister, M. Jakobsen, N. Gnecco, & J. Peters
IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6614–6630.
arXiv | bib | url
- 2022 – “Structure learning for directed trees”
Martin Jakobsen, R. Shah, P. Bühlmann, & J. Peters
Journal of Machine Learning Research, 23(159), 1–97.
arXiv | bib | url
- 2022 – “Conditional independence testing in Hilbert spaces with applications to functional data analysis”
A. R. Lundborg, R. D. Shah, & J. Peters
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 84(5), 1821–1850.
arXiv | bib | url
- 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
- 2022 – “A robustness test for estimating total effects with covariate adjustment”
Z. Su, & L. Henckel
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, 180, 1886–1895.
arXiv | bib | url
- 2022 – “Identifiability of sparse causal effects using instrumental variables”
N. Pfister, & J. Peters
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, 180, 1613–1622.
arXiv | bib | url
- 2022 – “Exploiting independent instruments: Identification and distribution generalization”
S. Saengkyongam, L. Henckel, N. Pfister, & J. Peters
Proceedings of the 39th International Conference on Machine Learning.
arXiv | bib | url
- 2022 – “Invariant ancestry search”
P. B. Mogensen, N. Thams, & J. Peters
Proceedings of the 39th International Conference on Machine Learning (ICML), 162, 15832–15857.
arXiv | bib | url
- 2022 – “Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning”
S. Weichwald, S. W. Mogensen, T. E. Lee, D. Baumann, O. Kroemer, I. Guyon, S. Trimpe, J. Peters, & N. Pfister
Proceedings of the NeurIPS 2021 Competition and Demonstration Track, Proceedings of Machine Learning Research (PMLR), 176, 246–258.
arXiv | bib | url
- 2022 – “Distributional robustness of k-class estimators and the PULSE”
M. Jakobsen, & J. Peters
The Econometrics Journal, 25(2), 404–432.
arXiv | bib
- 2021 – “Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game”
A. G. Reisach, C. Seiler, & S. Weichwald
Advances in Neural Information Processing Systems 34 (NeurIPS).
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 – “Foundations of structural causal models with cycles and latent variables”
S. Bongers, P. Forre, J. Peters, & J. M. Mooij
Annals of Statistics, 49(5), 2885–2915.
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 – “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, 598, 468–472.
bib | url
- 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 – “Compositional Abstraction Error and a Category of Causal Models”
E. F. Rischel, & S. Weichwald
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI).
arXiv | bib | url
- 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 – “Towards causal inference for spatio-temporal data: Conflict and forest loss in Colombia”
R. Christiansen, M. Baumann, T. Kümmerle, M. Mahecha, & J. Peters
Journal of the American Statistical Association, 117(538), 591–601.
arXiv | bib
- 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