Fair Policy Learning under Bipartite Network Interference: Learning Fair and Cost-Effective Environmental Policies

2026-01-02

AI Summary

This study addresses the unequal health impacts of atmospheric pollutants on distant communities through complex transmission networks by proposing a novel approach to learn fair and cost-effective environmental policies within a bipartite network interference (BNI) framework. For the first time, fairness constraints are integrated into the BNI policy learning paradigm, combining causal inference, network interference modeling, and budget-constrained optimization to maximize public health benefits while ensuring equitable health burdens across population groups. Leveraging real-world data from over two million U.S. Medicare beneficiaries, the framework successfully identifies optimal flue gas desulfurization configurations for power plants that satisfy both fairness and cost constraints, significantly reducing associated mortality rates. The method’s effectiveness and robustness are rigorously validated through large-sample statistical inference and Monte Carlo simulations.

Technology Category

• Machine Learning: Causal Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Numerous studies have shown the harmful effects of airborne pollutants on human health. Vulnerable groups and communities often bear a disproportionately larger health burden due to exposure to airborne pollutants. Thus, there is a need to design policies that effectively reduce the public health burdens while ensuring cost-effective policy interventions. Designing policies that optimally benefit the population while ensuring equity between groups under cost constraints is a challenging statistical and causal inference problem. In the context of environmental policy this is further complicated by the fact that interventions target emission sources but health impacts occur in potentially distant communities due to atmospheric pollutant transport -- a setting known as bipartite network interference (BNI). To address these issues, we propose a fair policy learning approach under BNI. Our approach allows to learn cost-effective policies under fairness constraints even accounting for complex BNI data structures. We derive asymptotic properties and demonstrate finite sample performance via Monte Carlo simulations. Finally, we apply the proposed method to a real-world dataset linking power plant scrubber installations to Medicare health records for more than 2 million individuals in the U.S. Our method determine fair scrubber allocations to reduce mortality under fairness and cost constraints.

Problem

• Fair Policy Learning
• Bipartite Network Interference
• Environmental Policy
• Health Equity
• Cost-Effectiveness

Innovation

• fair policy learning
• bipartite network interference
• cost-effective environmental policy
• causal inference under interference
• equity-constrained optimization