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.

翻译：大量研究表明空气污染物对人类健康具有有害影响。由于暴露于空气污染物，弱势群体和社区往往承受着不成比例的健康负担。因此，需要设计既能有效减轻公共卫生负担，又能确保政策干预具有成本效益的策略。在成本约束下，设计既能最大化惠及人口，又能确保群体间公平性的政策，是一个具有挑战性的统计与因果推断问题。在环境政策背景下，这一问题因以下事实而进一步复杂化：干预措施针对排放源，但健康影响却可能因大气污染物传输而发生在遥远的社区——这种情境被称为双部网络干扰（BNI）。为解决这些问题，我们提出了一种双部网络干扰下的公平策略学习方法。我们的方法允许在公平性约束下学习具有成本效益的策略，即使考虑到复杂的BNI数据结构。我们推导了渐近性质，并通过蒙特卡洛模拟展示了有限样本性能。最后，我们将所提出的方法应用于一个真实世界数据集，该数据集将美国发电厂洗涤器安装情况与超过200万人的医疗保险健康记录相关联。我们的方法确定了在公平性和成本约束下减少死亡率的公平洗涤器分配方案。