Joel L. Horowitz, Sokbae Lee
arXiv 25 Jul 2025 · Econometrics
arXiv:2507.19654 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Manski and Tamer (2002) Inference on regressions with interval data on a regressor or outcome | 0.843 | 3 | 3 | 100% |
| 2 | Corno, Hildebrandt, and Voena (2020) Age of Marriage, Weather Shocks, and the Direction of Marriage Payments | 0.811 | 4 | 2 | 100% |
| 3 | Kessler, Low, and Sullivan (2019) Incentivized Resume Rating: Eliciting Employer Preferences without Deception | 0.644 | 2 | 2 | 100% |
| 4 | Manski (1988) Identification of binary response models | 0.644 | 2 | 2 | 100% |
| 5 | Rosen and Ura (2025) Finite Sample Inference for the Maximum Score Estimand | 0.644 | 2 | 2 | 100% |
| 6 | Manski (2009) The 2009 Lawrence R. Klein Lecture: Diversified Treatment under Ambiguity | 0.511 | 2 | 1 | 100% |
| 7 | Cattaneo, Jansson, and Nagasawa (2020) Bootstrap-Based Inference for Cube Root Asymptotics | 0.405 | 1 | 1 | 100% |
| 8 | Chen and Lee (2018) Best subset binary prediction | 0.405 | 1 | 1 | 100% |
| 9 | Chen and Lee (2020) Binary classification with covariate selection through $_0$-penalised empirical risk minimisation | 0.405 | 1 | 1 | 100% |
| 10 | Dempster (2008) The Dempster–Shafer calculus for statisticians | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.