Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness
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Series
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SpeakersAndrii Babii (The University of North Carolina at Chapel Hill, United States)
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FieldEconometrics, Data Science and Econometrics
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LocationTinbergen Institute Amsterdam, Roeterseiland campus, E5.07
Amsterdam -
Date and time
March 20, 2026
12:30 - 13:30
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. Joint paper with Xi Chen, Eric Ghysels, and Rohit Kumar.
Check personal website of speaker:
Andrii Babii | Econometrics, Machine Learning, and Applied Economics