Inherent Trade-offs in the Fair Allocation of Treatments

TL;DR

This paper introduces a causal framework for learning fair treatment policies that balance fairness and overall benefit, demonstrating its application to educational data and highlighting the potential of affirmative action.

Contribution

It proposes a novel causal approach to optimize treatment policies under fairness constraints, addressing the fairness-benefit trade-off in decision-making.

Findings

Allowing preferential treatment to protected groups can enhance overall outcomes.

The framework effectively balances fairness and benefit in real-world data.

Application to student test scores shows practical policy design benefits.

Abstract

Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in human judgement by learning policies that improve the overall outcomes while providing fair treatment to protected classes. In this paper, we propose a causal framework that learns optimal intervention policies from data subject to fairness constraints. We define two measures of treatment bias and infer best treatment assignment that minimizes the bias while optimizing overall outcome. We demonstrate that there is a dilemma of balancing fairness and overall benefit; however, allowing preferential treatment to protected classes in certain circumstances (affirmative action) can dramatically improve the overall benefit while also preserving fairness. We apply our framework to data containing student outcomes on standardized tests and show how it can be used to design real-world policies that fairly improve student test scores. Our framework provides a principled way to learn fair treatment policies in real-world settings.