Hi, I'm Dominik.
My research centers around distribution shift, causal inference, and replicability.
I gratefully acknowledge support from the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and the Dieter Schwarz Foundation.
News:
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]
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]
Statistical inference can be fragile. How stable is your statistical model under distribution shift? Our paper got accepted at NeurIPS 2023! [GitHub] [Preprint]
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].
Is randomization inference after Mahalanobis matching justified? Our manuscript got accepted at Biometrika. [Paper]
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 ]
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]
I am thrilled to be awarded the David Cox Research Prize by the Royal Statistical Society! Thank you!
Recent and forthcoming talks
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