论文标题
识别和推理最终超大估计器的衡量劳动力市场公平性
Identification and Inference with Min-over-max Estimators for the Measurement of Labor Market Fairness
论文作者
论文摘要
这些注释显示了如何对人口统计学校准（DP）度量进行推断。尽管该度量是一个涉及最小值和最大计算的复杂统计量，但我们提出了这些功能的平滑近似，并得出其渐近分布。这些近似值及其梯度的极限会融合到真正的最大和最小函数的限制，无论它们存在何处。更重要的是，当真实的最大和最小函数无法区分时，近似值仍然是，并且它们在域中到处都提供有效的渐近推理。我们以有关如何计算DP的置信区间的一些指示，如何测试它是否低于0.8（美国平等就业机会委员会公平阈值），以及如何在A/B测试中进行推断。
These notes shows how to do inference on the Demographic Parity (DP) metric. Although the metric is a complex statistic involving min and max computations, we propose a smooth approximation of those functions and derive its asymptotic distribution. The limit of these approximations and their gradients converge to those of the true max and min functions, wherever they exist. More importantly, when the true max and min functions are not differentiable, the approximations still are, and they provide valid asymptotic inference everywhere in the domain. We conclude with some directions on how to compute confidence intervals for DP, how to test if it is under 0.8 (the U.S. Equal Employment Opportunity Commission fairness threshold), and how to do inference in an A/B test.
