Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness

2020/10/16 by Andrii Babii, Xi Chen, Babii, Andrii +5
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Applications (stat.AP) #Census and Population Estimation #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Machine Learning (stat.ML) #Methodology (stat.ME) #Neural Networks and Applications #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2010.08463

openalex publication_date 2020/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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