Fair Policy Targeting

• 成果类型: Article
• 署名作者: Viviano, Davide; Bradic, Jelena
• 署名单位: Stanford University; Harvard University; University of California System; University of California San Diego; University of California System; University of California San Diego
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2142591
• 发表日期: 2024
• 页码: 730-743
• 关键词: TREATMENT CHOICE; Semiparametric Efficiency; IMPACT

摘要

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 article addresses the question of the design of fair and efficient treatment allocation rules. We adopt the nonmaleficence 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. for this article are available online.