在选择性标签数据下，构建公平性-准确性权衡的可识别边界并实现统计推断。
Identification and Inference for Algorithmic Frontiers with Selective Labels
- 基于选择性观测下的损失函数，刻画了公平性与准确性的精确识别区域。
- 在可观测变量条件下无混杂假设下，实现点识别并提出去偏机器学习估计器。
- 适用于评估算法公平性与性能权衡的实证研究，尤其适合政策分析者。
本文在仅观测到部分个体结果的情况下，为公平性-准确性（FA）前沿提供了识别结果，并构建了用于检验假设和构建FA前沿置信集的统计推断工具。当选择过程不受限但损失以特定方式衡量时，我们给出了FA前沿的尖锐识别区域的刻画。在可观测变量条件下无混杂的假设下（且允许任意损失函数），我们实现了点识别，提出了去偏机器学习估计器，推导其渐近分布，并展示了如何用于对FA前沿进行推断。当前工作中，我们正将部分识别结果扩展至更广泛的损失函数类别。
原文摘要 · Abstract (English)
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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