arXiv 25 May 2020 · Econometrics · publishedJournal of the American Statistical Association (2022)
arXiv:2005.12395 · PDF · DOI · OpenAlex · Extracted main text
One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities across sensitive attributes such as age, gender, or race. This paper addresses the question of the design of fair and efficient treatment allocation rules. We adopt the non-maleficence perspective of first do no harm: we select the fairest allocation within the Pareto frontier. We cast the optimization into a mixed-integer linear program formulation, which can be solved using off-the-shelf algorithms. We derive regret bounds on the unfairness of the estimated policy function and small sample guarantees on the Pareto frontier under general notions of fairness. Finally, we illustrate our method using an application from education economics.
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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 | Kilbertus, N., M. R. Carulla, G. Parascandolo, M. Hardt, D. Janzing,… (2017) Avoiding discrimination through causal reasoning | 1.000 | 5 | 3 | 100% |
| 2 | Rambachan, A., J. Kleinberg, J. Ludwig, and S. Mullainathan (2020) An economic approach to regulating algorithms | 1.000 | 5 | 3 | 100% |
| 3 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? Empirical welfare maximization methods for treatment choice | 0.961 | 9 | 5 | 89% |
| 4 | Athey, S. and S. Wager (2021) Policy learning with observational data | 0.956 | 8 | 5 | 88% |
| 5 | Manski (2004) Statistical treatment rules for heterogeneous populations | 0.928 | 4 | 3 | 100% |
| 6 | Nabi, R., D. Malinsky, and I. Shpitser (2019) Learning optimal fair policies | 0.928 | 4 | 3 | 100% |
| 7 | Zhou, Z., S. Athey, and S. Wager (2018) Offline multi-action policy learning: Generalization and optimization | 0.874 | 12 | 5 | 67% |
| 8 | Kasy, M. and R. Abebe (2020) Fairness, equality, and power in algorithmic decision making | 0.737 | 3 | 3 | 67% |
| 9 | Elliott, G. and R. P. Lieli (2013) Predicting binary outcomes | 0.737 | 3 | 2 | 100% |
| 10 | Narita, Y (2021) Incorporating ethics and welfare into randomized experiments | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 64 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.