论文标题
在数据丰富的环境中，二进制选择具有不对称损失：理论和种族正义的应用
Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice
论文作者
论文摘要
我们研究具有不对称损失函数的数据丰富的环境中的二元选择问题。计量经济学文献涵盖了非参数二进制选择问题，但在数据丰富的环境中不提供具有计算吸引力的解决方案。机器学习文献具有许多算法，但主要集中在独立于协变量的损失功能上。我们表明，可以通过对逻辑回归或最新机器学习技巧的非常简单的基于损失的重新加权来实现关于二进制结果的理论上有效的决定。我们将分析应用于审前拘留中的种族正义。
We study the binary choice problem in a data-rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data-rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be achieved via a very simple loss-based reweighting of the logistic regression or state-of-the-art machine learning techniques. We apply our analysis to racial justice in pretrial detention.
