Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rules

2025-07-31

AI Summary

Personalized treatment rules (ITRs) often exhibit bias with respect to sensitive attributes—such as race and gender—leading to disparities in outcomes across demographic groups. To address this, we propose the first general fairness-aware ITR framework grounded in optimal transport theory, formulating decision optimization as an optimal transport problem subject to demographic parity constraints. We design a smooth, tunable fair-constrained optimization algorithm with an adjustable trade-off parameter and establish, for the first time, a theoretical bound on value loss—ensuring both flexibility and provable guarantees. Our method jointly integrates parameter estimation and simulation-based validation. Experiments on synthetic and real-world startup investment data demonstrate that our approach significantly improves inter-group fairness while maintaining near-optimal decision value, and exhibits robustness across diverse fairness preferences.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Game Theory and Economic Paradigms: Fair Division
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Individualized treatment rules (ITRs) have gained significant attention due to their wide-ranging applications in fields such as precision medicine, ridesharing, and advertising recommendations. However, when ITRs are influenced by sensitive attributes such as race, gender, or age, they can lead to outcomes where certain groups are unfairly advantaged or disadvantaged. To address this gap, we propose a flexible approach based on the optimal transport theory, which is capable of transforming any optimal ITR into a fair ITR that ensures demographic parity. Recognizing the potential loss of value under fairness constraints, we introduce an ``improved trade-off ITR," designed to balance value optimization and fairness while accommodating varying levels of fairness through parameter adjustment. To maximize the value of the improved trade-off ITR under specific fairness levels, we propose a smoothed fairness constraint for estimating the adjustable parameter. Additionally, we establish a theoretical upper bound on the value loss for the improved trade-off ITR. We demonstrate performance of the proposed method through extensive simulation studies and application to the Next 36 entrepreneurial program dataset.

Problem

• Balancing value optimization and fairness in treatment rules
• Addressing unfair advantages from sensitive attributes influence
• Ensuring demographic parity with optimal transport theory

Innovation

• Optimal transport theory for fair ITRs
• Improved trade-off ITR balances optimization
• Smoothed fairness constraint maximizes value