Yiqi Liu, Francesca Molinari, Amilcar Velez
arXiv 12 Jun 2026 · Econometrics
arXiv:2606.14977 · PDF · DOI · OpenAlex · Extracted main text
This paper provides identification results to characterize a fairness-accuracy (FA) frontier, and statistical inference tools to test hypotheses and build a confidence set for the FA-frontier, when outcomes are observed only for selected individuals. When the selection process is unrestricted but loss is measured in specific ways, we provide a characterization of the sharp identification region of the FA-frontier. Under an assumption of unconfoundedness conditional on observables (and unrestricted loss functions), we obtain point identification and propose a debiased machine learning estimator, derive its asymptotic distribution, and show how this can be used to carry out inference for the FA-frontier. In work in progress, we extend the partial identification results to a broader class of loss functions.
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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 | Liu, Yiqi and Francesca Molinari (2026) Inference for an Algorithmic Fairness-Accuracy Frontier self | 1.000 | 23 | 6 | 100% |
| 2 | Liang, Annie, Jay Lu, Xiaosheng Mu, and Kyohei Okumura (2026) Algorithm Design: A Fairness-Accuracy Frontier | 1.000 | 15 | 3 | 100% |
| 3 | Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 6 | 3 | 100% |
| 4 | Schneider, Rolf (1993) Convex Bodies: The Brunn-Minkowski Theory | 0.928 | 4 | 3 | 100% |
| 5 | Fang, Zheng and Andres Santos (2019) Inference on directionally differentiable functions | 0.874 | 8 | 2 | 100% |
| 6 | Bontemps, Christian, Thierry Magnac, and Eric Maurin (2012) Set identified linear models | 0.737 | 3 | 2 | 100% |
| 7 | Rambachan, Ashesh, Amanda Coston, and Edward H. Kennedy (2025) Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding | 0.737 | 3 | 2 | 100% |
| 8 | Molchanov, I (2017) Theory of Random Sets | 0.644 | 4 | 1 | 100% |
| 9 | van der Vaart, Aad W. and Jon A. Wellner (1996) Weak Convergence and Empirical Processes: With Applications to Statistics | 0.644 | 4 | 1 | 100% |
| 10 | Auerbach, Eric, Annie Liang, Max Tabord-Meehan, and Kyohei Okumura (2024) Testing the Fairness-Accuracy Improvability of Algorithms | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.