• Gauri Jain, Dominik Rothenhäusler, Kirk Bansak, and Elisabeth Paulson. CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets 

• Yujing Jeong, Rames Johari, Dominik Rothenhäusler, and Emily Fox.  Optimal Empirical Risk Minimization under Temporal Distribution Shifts

• Aditya Ghosh and Dominik Rothenhäusler (2025). Assumption-robust causal inference

• Ivy Zhang and Dominik Rothenhäusler (2025). Data quality or data quantity? Prioritizing data collection under distribution shift with the data usefulness coefficient

• Ying Jin, Naoki Egami, and Dominik Rothenhäusler (2024). Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization

• Yujin Jeong and Dominik Rothenhäusler (2024). Out-of-distribution generalization under random, dense distributional shifts.

• Ying Jin, Kevin Guo and Dominik Rothenhäusler (2023). Diagnosing the role of observable distribution shift in scientific replications. Under review.

• Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler (2023). Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models. AISTATS 2024.

• Suyash Gupta and Dominik​ ​Rothenhäusler (2023). The s-value: evaluating stability with respect to distributional shifts. Accepted at NeurIPS 2023.

• Dominik Rothenhäusler and Peter Bühlmann (2023).  Distributionally robust and generalizable inference. Statistical Science.

• Kevin Guo and Dominik Rothenhäusler (2023). On the statistical role of inexact matching in observational studies. Biometrika.

• Ying Jin and Dominik Rothenhäusler (2023). Modular regression. Accepted at Journal of Machine Learning Research.

• Yujin Jeong and Dominik Rothenhäusler (2022). Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty. Under review.

• Jaime Roquero Gimenez and Dominik Rothenhäusler (2022). Causal aggregation: estimation and inference of causal effects by constraint-based data fusion. Journal of Machine Learning Research.

• Ying Jin and Dominik​ ​Rothenhäusler (2021). Tailored inference for finite populations: conditional validity and transfer across distributions. Biometrika.

• Dominik​ ​Rothenhäusler (2020). Model selection for estimation of causal parameters. Accepted at Electronic Journal of Statistics, 2024.

• Dominik​ ​Rothenhäusler and Bin Yu (2019). Incremental causal effects. Preprint arXiv:1907.13258.

• Dominik​ ​Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann, and Jonas Peters (2021). Anchor regression: heterogeneous data meets causality. Journal of the Royal Statistical Society: Series B. Preprint arXiv:1801.06229.

• Dominik​ ​Rothenhäusler,​ ​Peter​ ​Bühlmann,​ ​and​ ​Nicolai​ ​Meinshausen (2018).​ ​Causal​ ​dantzig:​ ​fast​ ​inference​ ​in linear​ ​structural​ ​equation​ ​models​ ​with​ ​hidden​ ​variables​ ​under​ ​additive​ ​interventions.​ ​ ​​Annals​ ​of​ ​Statistics​.​ ​Preprint arXiv:1706.06159. 

• Dominik​ ​Rothenhäusler*,​ ​Jan​ ​Ernest*,​ ​and​ ​Peter​ ​Bühlmann (2018).​ ​Causal​ ​inference​ ​in​ ​partially​ ​linear structural​ ​equation​ ​models:​ ​identifiability​ ​and​ ​estimation.​ ​​ ​​Annals​ ​of​ ​Statistics​. Preprint​ ​arXiv:1607.05980. 

• Dominik​ ​Rothenhäusler,​ ​Nikolaus​ ​Schweizer,​ ​and​ ​Nora​ ​Szech (2017).​ ​Guilt​ ​in​ ​voting​ ​and​ ​public​ ​good games.​ ​​European​ ​Economic​ ​Review​, 101:664-681.

• Michael​ ​Sokolov,​ ​Jonathan​ ​Ritscher,​ ​Nicola​ ​MacKinnon,​ ​Jean-Marc​ ​Bielser,​ ​David​ ​Brühlmann, Dominik​ ​Rothenhäusler,​ ​Gian​ ​Thanei,​ ​Miroslav​ ​Soos,​ ​Matthieu​ ​Stettler,​ ​Jonathan​ ​Souquet,​ ​et​ ​al. (2017).​ ​Robust factor​ ​selection​ ​in​ ​early​ ​cell​ ​culture​ ​process​ ​development​ ​for​ ​the​ ​production​ ​of​ ​a biosimilar​ ​monoclonal​ ​antibody.​ ​​Biotechnology​ ​progress​,​ ​33(1):181-191. 

• Dominik​ ​Rothenhäusler,​ ​Nicolai​ ​Meinshausen,​ ​and​ ​Peter​ ​Bühlmann (2016).​ ​Confidence​ ​intervals​ ​for​ ​maximin​ ​effects​ ​in​ ​inhomogeneous​ ​large-scale​ ​data.​ ​In​ ​​Statistical​ ​Analysis​ ​for​ ​High-Dimensional Data​,​ ​pages​ ​255-277. 

• Dominik​ ​Rothenhäusler*, ​Christina​ ​Heinze*,​ ​Jonas​ ​Peters,​ ​and​ ​Nicolai​ ​Meinshausen (2015).​ ​Backshift: Learning​ ​causal​ ​cyclic​ ​graphs​ ​from​ ​unknown​ ​shift​ ​interventions.​ ​In​ ​​Advances​ ​in​ ​Neural​ ​Information Processing​ ​Systems​,​ ​pages​ ​1513-1521. 

*Authors contributed equally.