Empirical Inference Conference Paper Algorithmic recourse in partially and fully confounded settings through bounding counterfactual effects von Kügelgen, J., Agarwal, N., Zeitler, J., Mastouri, A., Schölkopf, B. ICML 2021 Workshop on Algorithmic Recourse, July 2021 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes Laumann, F., von Kügelgen, J., Barahona, M. 7th International Conference on Time Series and Forecasting (ITISE 2021), July 2021 (Published) arXiv BibTeX

Empirical Inference Conference Paper Visual Representation Learning Does Not Generalize Strongly Within the Same Domain Schott, L., von Kügelgen, J., Träuble, F., Gehler, P., Russell, C., Bethge, M., Schölkopf, B., Locatello, F., Brendel, W. ICLR 2021 - Workshop on Generalization beyond the training distribution in brains and machines, May 2021 (Published) URL BibTeX

Empirical Inference Article Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects von Kügelgen*, J., Gresele*, L., Schölkopf, B. IEEE Transactions on Artificial Intelligence, 2(1):18-27, IEEE Computer Society, 2021, *equal contribution (Published) arXiv DOI URL BibTeX

Empirical Inference Probabilistic Learning Group Conference Paper Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I. Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 265-277, (Editors: H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin), Curran Associates, Inc., 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Semi-supervised learning, causality, and the conditional cluster assumption von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B. Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI) , 124:1-10, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020, *also at NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals Laumann, F., von Kügelgen, J., Barahona, M. ICLR 2020 Workshop "Tackling Climate Change with Machine Learning", April 2020 (Published) arXiv PDF BibTeX

Empirical Inference Conference Paper Towards causal generative scene models via competition of experts von Kügelgen*, J., Ustyuzhaninov*, I., Gehler, P., Bethge, M., Schölkopf, B. ICLR 2020 Workshop "Causal Learning for Decision Making", April 2020, *equal contribution (Published) arXiv PDF BibTeX

Empirical Inference Conference Paper Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks von Kügelgen, J., Rubenstein, P. K., Schölkopf, B., Weller, A. NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making, December 2019 (Published) arXiv Poster URL BibTeX

Empirical Inference Conference Paper Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features von Kügelgen, J., Mey, A., Loog, M. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89:1361-1369, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (Published) PDF Poster URL BibTeX