Hans-Georg Müller of UC Davis : Geodesic Causal Inference

Posted by Ping-Shou Zhong , part of the Statistics and Data Science Seminar.

At

Oct. 21, 2026, 4 p.m.

In

636 SEO

Abstract

Adjusting for confounding and imbalance when establishing statistical relationships is an important task. Causal inference methods have emerged as a popular tool to achieve this for regression relationships with scalar responses and also distributional responses. We introduce a general framework for causal inference when responses reside in geodesic metric spaces, drawing on a novel geodesic calculus, which facilitates the quantification of treatment effects through the concept of geodesic average treatment effect (GATE). Employing Fréchet regression, we obtain doubly robust estimation of GATE with rates of convergence. Theoretical results are complemented with various data illustrations. Based on joint work with Daisuke Kuriso, Yidong Zhou and Taisuke Otsu.