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 logistic regression or state-of-the-art machine learning techniques. We apply our analysis to algorithmic fairness in pretrial detentions.

翻译：我们研究了在数据丰富环境下，基于非对称损失函数的二元选择问题。计量经济学文献涵盖了非参数二元选择问题，但未提供在数据丰富环境中计算上具有吸引力的解决方案。机器学习文献虽拥有众多算法，但主要关注与协变量无关的损失函数。我们证明，通过对逻辑回归或前沿机器学习技术进行基于损失的简单重加权，可以实现具有一般损失函数的二元结果在理论上的有效决策。我们将该分析应用于审前拘留中的算法公平性问题。