Hi, I'm Dominik.

My research centers around distribution shift, causal inference, and replicability. I  completed my Ph.D. studies under the supervision of Nicolai Meinshausen and Peter Bühlmann at ETH Zürich in Summer 2018. Prior to joining the Stanford faculty, I completed a postdoc with Bin Yu at UC Berkeley.

I gratefully acknowledge support from the Dieter Schwarz Foundation, the Chamber Foundation, and the David Huntington Fellowship.

News:

1. Many existing approaches for estimating parameters in settings with distributional shifts operate under an invariance assumption. For example, under covariate shift, it is assumed that p(y|x) remains invariant. We refer to such distribution shifts as sparse, since they may be substantial but affect only a part of the data generating system. In contrast, in real-world settings, shifts might be dense. That is, they arise through numerous small and random changes in the population and environment. We discuss out-of-distribution generalization under such random, dense shifts [Preprint].

2. Many researchers have identified distribution shift as a likely contributor to the replication crisis. We built a set of tools to diagnose the role of observable distribution shifts in scientific replications. Surprisingly, we find little evidence that that distribution shift in observed covariates contributes appreciably to non-replicability. [Preprint, Data, R-package, Shiny app]

3. We developed a modular framework for statistical inference in linear models. At a high level, our method follows the routine: (i) decomposing the regression task into several sub-tasks, (ii) fitting the sub-task models, and (iii) using the sub-task models to provide an improved estimate for the original regression problem. Our paper got accepted at JMLR! [Preprint]

4. Statistical inference can be fragile. How stable is your statistical model under distribution shift? Our paper got accepted at NeurIPS 2023! [GitHub] [Preprint]

5. Do you want to conduct statistical inference for a fixed set of units, such as the current customers of a company?  Targeting statistical inference to the units of interest can improve precision by more than 50%! Our manuscript got accepted at Biometrika [Link].

6. Is randomization inference after Mahalanobis matching justified? Our manuscript got accepted at Biometrika. [Paper]

7. Do you want to integrate causal evidence from different experiments, instrumental variables, and regression adjustments to get a more complete causal picture? Our manuscript got accepted at JMLR. [Link ]

8. Many researchers evaluate the stability of a statistical finding by running multiple analyses with differently specified models. Can we give rigorous guarantees for this practice? [GitHub] [Preprint]

9. I am thrilled to be awarded the David Cox Research Prize by the Royal Statistical Society! Thank you!

Recent and forthcoming talks

• Talk at JSM 2025

• Talk at IMS Inaugural Workshop on Frontiers in Statistical Machine Learning 2025

• Talk at Peter Bühlmann's 60th birthday

• Talk at ACIC 2025

• Talk at ICSDS 2024

• Talk at JSM 2024

• Talk at the "Shanghai Workshop on Robustness meets Causality" (Summer 2024)

• Talk at the Warwick workshop on heterogeneous and distributed data (Summer 2024)

• Talk at the Veridical Data Science Workshop at Berkeley (Spring 2024)

• Talk at the University of Washington, Department of Biostatistics (Spring 2024)

• Talk at the Berkeley Department of Political Science

• Talk at Stanford's AFT Laboratory (Spring 2024)

• Talk at the Berkeley-Stanford Joint Colloquium 2024

• Talk at ACIC 2024

• Talk at ICSDS 2023

• Talk at CMStatistics 2023

• Talk at INFORMS

• Talk at JSM 2023

• Talk at Copenhagen University

• Talk at Nordstat 2023

• Talk at the New England Statistics Symposium 2023

• Talk at the Statistics Department at UC Davis

• Talk at the Seminar for Statistics at ETH Zurich

• Talk at the MSRI workshop on "Foundations of Stable, Generalizable and Transferable Statistical Learning"

• Talk at the Stanford Statistics Seminar

• Talk at the Statistics Seminar of KAUST

• Talk at the Online Causal Inference Seminar

• Talk at the CSLI Workshop

• Talk at the Simons workshop "Statistics in the Big Data Era"

Email: rdominik {at} stanford.edu