Binary classification with the maximum score model and linear programming - Joel Horowitz; Sokbae Lee (Centre for Microdata Methods and Practice (cemmap), The Institute for Fiscal Studies (IFS), 2025)
This paper presents a computationally efficient method for binary classification using Manski's (1975,1985) maximum score model when covariates are discretely distributed and parameters are partially but not point identified. We establish conditions under which it is minimax optimal to allow for either non-classification or random classification and derive finite-sample and asymptotic lower bounds on the probability of correct classification. We also describe an extension of our method to continuous covariates. Our approach avoids the computational difficulty of maximum score estimation by reformulating the problem as two linear programs. Compared to parametric and nonparametric methods, our method balances extrapolation ability with minimal distributional assumptions. Monte Carlo simulations and empirical applications demonstrate its effectiveness and practical relevance.
Keywords: 330; Binary classification; maximum score estimation; partial identification; finite-sample and asymptotic inference; extrapolation