Anders Bredahl Kock, David Preinerstorfer
arXiv 31 Jan 2024 · Econometrics · publishedJournal of Econometrics (2026)
arXiv:2401.17909 · PDF · DOI · OpenAlex · Extracted main text
A decision maker typically (i) incorporates training data to learn about the relative effectiveness of treatments, and (ii) chooses an implementation mechanism that implies an “optimal” predicted outcome distribution according to some target functional. Nevertheless, a fairness-aware decision maker may not be satisfied achieving said optimality at the cost of being “unfair" against a subgroup of the population, in the sense that the outcome distribution in that subgroup deviates too strongly from the overall optimal outcome distribution. We study a framework that allows the decision maker to regularize such deviations, while allowing for a wide range of target functionals and fairness measures to be employed. We establish regret and consistency guarantees for empirical success policies with (possibly) data-driven preference parameters, and provide numerical results. Furthermore, we briefly illustrate the methods in two empirical settings.
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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 | Kitagawa, T. and Tetenov, A (2021) Equality-minded treatment choice | 0.899 | 11 | 4 | 73% |
| 2 | Viviano, D. and Bradic, J (2023) Fair policy targeting | 0.874 | 9 | 2 | 100% |
| 3 | Lyons, E. and Zhang, L (2017) The impact of entrepreneurship programs on minorities | 0.874 | 6 | 2 | 100% |
| 4 | Kock, A. B., Preinerstorfer, D. and Veliyev, B (2022) Functional sequential treatment allocation self | 0.843 | 4 | 3 | 75% |
| 5 | Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenst… (2017) A convex framework for fair regression | 0.737 | 3 | 2 | 100% |
| 6 | Bilias, Y (2000) Sequential testing of duration data: the case of the pennsylvania ‘reemployment bonus’ experiment | 0.737 | 3 | 2 | 100% |
| 7 | Kock, A. B., Preinerstorfer, D. and Veliyev, B (2023) Treatment recommendation with distributional targets self | 0.693 | 9 | 5 | 33% |
| 8 | Kitagawa, T. and Tetenov, A (2018) Who should be treated? Empirical welfare maximization methods for treatment choice | 0.644 | 3 | 2 | 67% |
| 9 | Aliprantis, C. and Border, K (2006) Infinite Dimensional Analysis | 0.585 | 5 | 4 | 20% |
| 10 | Fang, E. X., Wang, Z. and Wang, L (2023) Fairness-oriented learning for optimal individualized treatment rules | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 45 scored citations.