Topological reconstruction of Rubin multiple imputation via coarse proximity, Seifert van Kampen gluing and Hurewicz invariants

Abstract

Rubin multiple imputation (MI) generates plausible data completions to account for uncertainty and statistical variability but provides little insight into their global organization. We introduce a topological reconstruction approach that complements MI by examining the ensemble of completed datasets. Individual imputations are represented as points in a reconstruction space whose coordinates summarize statistical properties. Concepts from coarse geometry and algebraic topology are then used to characterize relationships among alternative imputations across multiple scales. Coarse proximity (CP) defines large-scale neighborhoods, generating graphs in which nodes represent completed datasets and edges connect sufficiently similar imputations. Seifert van Kampen gluing provides a conceptual interpretation of how local reconstructions assemble into globally coherent structures, whereas Hurewicz-type invariants quantify persistent connectivity patterns. Synthetic multivariate biomedical datasets representing adult cardiometabolic cohorts were generated with controlled missingness levels. Multiple stochastic imputations were projected into the reconstruction space and analyzed through CP graphs, connected components, cycle descriptors and scale-dependent topological measures. MI generated structured spaces with distinct connectivity patterns rather than homogeneous clouds of solutions. Topological descriptors remained stable despite local numerical variability, whereas increasing missingness produced transitions in reconstruction-space connectivity together with progressive deterioration of reconstruction accuracy. Our approach could be applied to biological and social networks, systems medicine, ecological modeling and other domains in which large-scale structural organization contributes to reliable inference.