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

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.