Identification and Inference for Algorithmic Frontiers with Selective Labels

2026-06-12

AI Summary

This study addresses the fairness–accuracy (FA) trade-off under selective labeling, where outcomes are observed only for a subset of individuals. Under a conditional unconfoundedness assumption, it establishes, for the first time, a sharp identification region for the FA frontier and achieves point identification under general loss functions. By integrating debiased machine learning, semiparametric inference, and causal identification boundary analysis, the authors develop an efficient estimation procedure and derive the asymptotic distribution of the FA frontier, enabling valid hypothesis testing and confidence set construction. This work extends partial identification frameworks to a broad class of loss functions, providing both theoretical foundations and inferential tools for fair machine learning in settings with selective labels.

Abstract

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.

Problem

• fairness-accuracy frontier
• selective labels
• identification
• statistical inference
• unconfoundedness

Innovation

• Selective Labels
• Fairness-Accuracy Frontier
• Sharp Identification
• Debiased Machine Learning
• Statistical Inference