A0665
Title: Difference-in-differences for random objects
Authors: Daisuke Kurisu - The University of Tokyo (Japan) [presenting]
Yidong Zhou - University of California, Davis (United States)
Taisuke Otsu - London School of Economics (United Kingdom)
Hans-Georg Mueller - University of California Davis (United States)
Abstract: Geodesic difference-in-differences (GDID) is proposed, a novel DID framework for outcomes in geodesic metric spaces, such as distributions, networks, and manifold-valued data. The geodesic average treatment effect is first introduced on the treated (ATT) as the causal estimand. Then, the identification of the geodesic ATT is established, and the convergence rate of its sample versions is derived. This framework can be further extended to the case of staggered DID settings, allowing for multiple time periods and varying treatment timings. To illustrate the practical utility of the GDID, the proposed method is applied to several real-world datasets.
