Statistics and Data Science Seminar
Dogyoon Song
University of California at Davis
Regression adjustment with high-dimensional covariates
Abstract: Regression adjustment is a classical technique in causal inference that leverages covariates to improve precision of estimators in randomized controlled trials (RCTs) and to adjust for confounding in observational studies. While well-understood in low-dimensional settings, its behavior in modern high-dimensional regimes---where the number of covariates may be comparable to or even exceed the number of observations---remains underexplored. In particular, existing theoretical results are largely asymptotic, often rely on residual-based arguments, and provide limited insights into finite-sample inference especially when $p>n$.
In this talk, we revisit regression adjustment for the average treatment effecting (ATE) estimation under complete randomization with many covariates, in a design-based, finite-population framework, via two vignettes. First, we introduce a novel theoretical perspective on the asymptotic properties of regression adjustment through a Neumann-series decomposition, yielding a refined analysis in the $pn$ settings. Leveraging concentration of measure tools, we quantify uncertainty without relying on classical asymptotic variance estimation, and further control the design bias of estimators via Stein's method of exchangeable pairs. Time permitting, we will discuss potential extensions and ongoing work.
Wednesday November 19, 2025 at 4:15 PM in Zoom