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

72 Pages Posted: 3 Nov 2020

See all articles by Andrii Babii

Andrii Babii

University of North Carolina at Chapel Hill

Eric Ghysels

University of North Carolina Kenan-Flagler Business School; University of North Carolina (UNC) at Chapel Hill - Department of Economics

Xi Chen

University of North Carolina (UNC) at Chapel Hill

rohit kumar

affiliation not provided to SSRN

Date Written: October 1, 2020

Abstract

The importance of asymmetries in prediction problems arising in economics has been recognized for a long time. In this paper, we focus on binary choice problems in a data-rich environment with general loss functions. In contrast to the asymmetric regression problems, the binary choice with general loss functions and high-dimensional datasets is challenging and not well understood. Econometricians have studied binary choice problems for a long time, but the literature does not offer computationally attractive solutions in data-rich environments. In contrast, the machine learning literature has many computationally attractive algorithms that form the basis for much of the automated procedures that are implemented in practice, but it is focused on symmetric loss functions that are independent of individual characteristics. One of the main contributions of our paper is to show that the theoretically valid predictions of binary outcomes with arbitrary loss functions can be achieved via a very simple reweighting of the logistic regression, or other state-of-the-art machine learning techniques, such as boosting or (deep) neural networks. We apply our analysis to racial justice in pretrial detention.

Suggested Citation

Babii, Andrii and Ghysels, Eric and Chen, Xi and kumar, rohit, Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice (October 1, 2020). CEPR Discussion Paper No. DP15418, Available at SSRN: https://ssrn.com/abstract=3723642

Andrii Babii (Contact Author)

University of North Carolina at Chapel Hill ( email )

Gardner Hall, CB 3305
Chapel Hill, NC 27514
United States

Eric Ghysels

University of North Carolina Kenan-Flagler Business School ( email )

Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States

University of North Carolina (UNC) at Chapel Hill - Department of Economics ( email )

Gardner Hall, CB 3305
Chapel Hill, NC 27599
United States
919-966-5325 (Phone)
919-966-4986 (Fax)

HOME PAGE: http://https://eghysels.web.unc.edu/

Xi Chen

University of North Carolina (UNC) at Chapel Hill ( email )

102 Ridge Road
Chapel Hill, NC NC 27514
United States

Rohit Kumar

affiliation not provided to SSRN

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