Fair Policy Targeting

TL;DR

This paper develops a method for designing fair treatment allocation policies in social programs, balancing fairness and efficiency while providing theoretical guarantees and practical solutions.

Contribution

It introduces a mixed-integer linear programming approach for fair policy design with regret bounds and small sample guarantees, applied to education economics.

Findings

Optimized fair treatment policies with Pareto efficiency.

Theoretical bounds on unfairness and sample guarantees.

Practical application demonstrating effectiveness in education economics.

Abstract

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.