“Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization,” Dominik Rothenhaeusler, Stanford University

QUANTITATIVE RESEARCH METHODS WORKSHOP

Abstract: Generalizing scientific findings across populations is central to science and policy. A common approach is to reweight observed covariates, implicitly assuming that only the covariate distribution changes across settings. Analyzing 680 studies across 65 sites, we show that shifts in the outcome conditional on covariates (Y | X) are common, but the strength of these shifts can be predicted from changes in observed covariates. I’ll introduce a statistical framework that explains this predictive, rather than merely explanatory, role of covariates in effect generalization, and discuss how it can guide the development of new methods for external validity. (This is joint work with Ying Jin, Naoki Egami, Yujin Jeong, Anna Lyubarskaja, and Ivy Zhang.)

Dominik Rothenhaeusler is an Assistant Professor of Statistics at Stanford University. His research focuses on distribution shift and causal inference, with broader interests in heterogeneous data, high-dimensional statistics, and graphical models. He earned his Ph.D. from ETH Zürich in 2018 under the supervision of Nicolai Meinshausen and Peter Bühlmann, and completed a postdoctoral fellowship with Bin Yu at UC Berkeley. He is a recipient of the Royal Statistical Society’s David Cox Research Prize, an early-career award from the Schwarz Foundation, a Chamber Fellow, and the David Huntington Dean’s Scholar.

The Quantitative Research Methods Workshop is open to the Yale community. To receive regular announcements and invitations to attend, please subscribe at this link.