Central Limit Theorem for Gram-Schmidt Random Walk Design

We prove a central limit theorem for the Horvitz--Thompson estimator based on the Gram--Schmidt Walk (GSW) design, recently developed in Harshaw et al.(2022). In particular, we consider the version of the GSW design which uses~\emph{randomized pivot order}, thereby answering an open question raised in the same article. We deduce this under minimal and global assumptions on involving {\em only} the problem parameters such as the (sum) potential outcome vector and the covariate matrix. As an interesting consequence of our analysis we also obtain the precise limiting variance of the estimator in terms of these parameters which is {\em smaller} than the previously known upper bound. The main ingredients are a simplified \emph{skeletal} process approximating the GSW design and concentration phenomena for random matrices obtained from random sampling using the Stein's method for exchangeable pairs