Andrii Babii, Xi Chen, Eric Ghysels, Rohit Kumar
arXiv 16 Oct 2020 · Econometrics · publishedQuantitative Economics (2026) · 4 citations (OpenAlex)
arXiv:2010.08463 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | G. Elliott and R. P. Lieli (2013) Predicting binary outcomes | 1.000 | 6 | 3 | 100% |
| 2 | S. Boucheron, O. Bousquet, and G. Lugosi (2005) Theory of classification: A survey of some recent advances | 0.843 | 4 | 3 | 75% |
| 3 | V. Koltchinskii (2011) Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: Ecole d'Eté de Probabilités de Saint-Flour XXXV… | 0.843 | 15 | 6 | 60% |
| 4 | M. H. Farrell, T. Liang, and S. Misra (2021) Deep neural networks for estimation and inference: Application to causal effects and other semiparametric estimands | 0.843 | 3 | 3 | 100% |
| 5 | A. Rambachan, J. Kleinberg, J. Ludwig, and S. Mullainathan (2020) An economic approach to regulating algorithms | 0.811 | 4 | 2 | 100% |
| 6 | J. Friedman, T. Hastie, R. Tibshirani, et al (2001) The elements of statistical learning, volume 1 | 0.737 | 5 | 2 | 60% |
| 7 | J.-Y. Audibert and A. B. Tsybakov (2007) Fast learning rates for plug-in classifiers | 0.737 | 3 | 3 | 67% |
| 8 | P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe (2006) Convexity, classification, and risk bounds | 0.644 | 2 | 2 | 100% |
| 9 | P. F. Christoffersen and F. X. Diebold (1996) Further results on forecasting and model selection under asymmetric loss | 0.644 | 2 | 2 | 100% |
| 10 | P. F. Christoffersen and F. X. Diebold (1997) Optimal prediction under asymmetric loss | 0.644 | 2 | 2 | 100% |
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