BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. - Dominik Rothenhäusler; Christina Heinze; Jonas Peters; Nicolai Meinshausen (NIPS, 2015)

Causal Aggregation: Estimation and Inference of Causal Effects by Constraint-Based Data Fusion. - Jaime Roquero Gimenez; Dominik Rothenhäusler (J. Mach. Learn. Res., 2022)

The s-value: evaluating stability with respect to distributional shifts. - Suyash Gupta 002; Dominik Rothenhäusler (NeurIPS, 2023)

Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models. - Kirk Bansak; Elisabeth Paulson; Dominik Rothenhäusler (CoRR, 2023)

Modular Regression: Improving Linear Models by Incorporating Auxiliary Data. - Ying Jin; Dominik Rothenhäusler (J. Mach. Learn. Res., 2023)

Learning Under Random Distributional Shifts. - Kirk C. Bansak; Elisabeth Paulson; Dominik Rothenhäusler (AISTATS, 2024)

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

Optimal Empirical Risk Minimization under Temporal Distribution Shifts. - Yujin Jeong; Ramesh Johari; Dominik Rothenhäusler; Emily B. Fox (CoRR, 2025)