4 research outputs found
Fair Policy Targeting
One of the major concerns of targeting interventions on individuals in social
welfare programs is discrimination: individualized treatments may induce
disparities on 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
propose to select the fairest allocation within the Pareto frontier. We provide
envy-freeness justifications to novel counterfactual notions of fairness. We
discuss easy-to-implement estimators of the policy function, by casting the
optimization into a mixed-integer linear program formulation. We derive regret
bounds on the unfairness of the estimated policy function, and small sample
guarantees on the Pareto frontier. Finally, we illustrate our method using an
application from education economics