Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice

• Tác giả: Andrii Babii, Xi Chen, Eric Ghysels, Rohit Kumar
• Ngôn ngữ: eng
• Ký hiệu phân loại: 155.82 Ethnic differences
• Thông tin xuất bản: 2020
• Bộ sưu tập: Metadata
• ID: 165403

Tóm tắt

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.