Before revision:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1)  construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
After revision:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1) efficiently construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.