L. Kook, N. Pfister: Instrumental Variable Estimation of Distributional Causal Effects.
Preprint ArXiv.
M. Lazzaretto, J. Peters, N. Pfister: Invariant Subspace Decomposition.
Preprint ArXiv.
S. Huang, J. Peters, N. Pfister: Causal Change Point Detection and Localization.
Preprint ArXiv.
N. Pfister, P. Bühlmann: Extrapolation-Aware Nonparametric Statistical Inference.
Preprint ArXiv.
A. R. Lundborg, N. Pfister: Perturbation-based Effect Measures for Compositional Data.
Preprint ArXiv.
N. Gnecco, J. Peters, S. Engelke, N. Pfister: Boosted Control Functions.
Preprint ArXiv.
S. Saengkyongam, E. Rosenfeld, P. Ravikumar, N. Pfister, J. Peters: Identifying Representations for Intervention Extrapolation. In Proceedings of the 12th International Conference on Learning Representations (accepted).
Preprint ArXiv.
S. Saengkyongam, N. Pfister, P. Klasnja, S. Murphy, J. Peters: Effect-Invariant Mechanisms for Policy Generalization. Journal of Machine Learning Research, 25(34), 1-36.
https://jmlr.org/papers/volume25/23-0802/23-0802.pdf, ArXiv.
S. Huang, E. Ailer, N. Kilbertus, N. Pfister: Supervised Learning and Model Analysis with Compositional Data. PLOS Computational Biology, 19(6), 1-19.
https://doi.org/10.1371/journal.pcbi.1011240, ArXiv.
N. Thams, S. Saengkyongam, N. Pfister, J. Peters: Statistical Testing under Distributional Shifts. Journal of the Royal Statistical Society: Series B, 85(3), 597-663.
https://doi.org/10.1093/jrsssb/qkad018, ArXiv.
S. Saengkyongam, N. Thams, J. Peters, N. Pfister: Invariant Policy Learning: A causal perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(7), 8606-8620.
https://doi.org/10.1109/TPAMI.2022.3232363, ArXiv.
A. Ruaud, N. Pfister, R. Ley, N. Youngblut: Interpreting tree ensemble machine learning models with endoR. PLOS Computational Biology, 18(12), 1-39.
https://doi.org/10.1371/journal.pcbi.1010714, BioRxiv.
S. Saengkyongam, L. Henckel, N. Pfister, J. Peters: Exploiting Independent Instruments: Identification and Distribution Generalization. In Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR 162, 18935-18958.
https://proceedings.mlr.press/v162/saengkyongam22a.html, ArXiv.
J. Peters, S. Bauer, N. Pfister: Causal Models for Dynamical Systems. In Probabilistic and Causal Inference: The Works of Judea Pearl, pp. 671-690.
https://doi.org/10.1145/3501714.3501752, ArXiv.
S. Weichwald and S. W. Mogensen, T. E. Lee, D. Baumann, O. Kroemer, I. Guyon, S. Trimpe, J. Peters, N. Pfister: Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR 176, 246-258.
https://proceedings.mlr.press/v176/weichwald22a.html, ArXiv.