Dominik Rothenhäusler

Augmented Inverse Hybrid Weighting: robust inference under deterministic and random distribution shifts

Which Covariates to Adjust for? Specification-robust Causal Inference in Observational Studies

Nonparametric Regression for Random Unbiased Perturbations

CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets

Optimal Empirical Risk Minimization under Temporal Distribution Shifts

Predicting data value before collection: A coefficient for prioritizing sources under random distribution shift

Diagnosing the role of observable distribution shift in scientific replications

Out-of-distribution generalization under random, dense distributional shifts

Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty

Beyond reweighting: On the predictive role of covariate shift in effect generalization

Learning under random distributional shifts

Tailored inference for finite populations: conditional validity and transfer across distributions

Model selection for estimation of causal parameters

Modular Regression: Improving Linear Models by Incorporating Auxiliary Data

The s-value: evaluating stability with respect to distributional shifts

Distributionally robust and generalizable inference

On the statistical role of inexact matching in observational studies

Causal aggregation: estimation and inference of causal effects by constraint-based data fusion

Anchor regression: heterogeneous data meets causality

Causal Dantzig: fast inference in linear structural equation models with hidden variables under additive interventions

Causal inference in partially linear structural equation models: identifiability and estimation

Guilt in voting and public good games

BackShift: learning causal cyclic graphs from unknown shift interventions

Confidence intervals for maximin effects in inhomogeneous large-scale data