Speaker
Description
In this talk, we propose geodesic difference-in-differences (GDID), a novel DID framework for outcomes in geodesic metric spaces, such as distributions, networks, and manifold-valued data. To address the absence of algebraic operations in these spaces, we use geodesics as proxies for differences and introduce the geodesic average treatment effect on the treated (ATT) as the causal estimand. We establish the identification of the geodesic ATT and derive the convergence rate of its sample versions, employing tools from metric geometry and empirical process theory. To illustrate the practical utility of geodesic DID, we analyze health impacts of the Soviet Union’s collapse using age-at-death distributions and assess effects of U.S. electricity market liberalization on electricity generation compositions. (Joint work with Yidong Zhou, Taisuke Otsu, and Hans-Georg Mueller)