Marginalized groups are exposed to disproportionately high levels of air pollution. In this context, robust evaluations of the heterogeneous health impacts of air pollution regulations are key to justifying and designing maximally protective future interventions. Such evaluations are complicated by two key issues: 1) much of air pollution regulatory policy is focused on intervening on large emissions generators while resulting health impacts are measured in exposed populations; 2) due to air pollution transport, an intervention on one emissions generator can impact geographically distant communities. In causal inference, such a scenario has been described as that of bipartite network interference (BNI). To our knowledge, no literature to date has considered how to estimate heterogeneous causal effects with BNI. First, we propose, implement, and evaluate causal estimators for subgroup-specific treatment effects via augmented inverse propensity weighting and G-computation methods in the context of BNI. Second, we design and implement an empirical Monte Carlo simulation approach for BNI through which we evaluate the performance of the proposed estimators. Third, we use the proposed methods to estimate the causal effects of flue gas desulfurization scrubber installations on coal-fired power plants on ischemic heart disease hospitalizations among 27,312,190 Medicare beneficiaries residing across 29,034 U.S. ZIP codes. While we find no statistically significant effect of scrubbers in the full population, we do find protective effects in marginalized groups. For high-poverty and predominantly non-white ZIP codes, scrubber installations at their most influential power plants, when less-influential plants are untreated, are found to result in statistically significant decreases in IHD hospitalizations, with reduction ranging from 6.4 to 43.1 hospitalizations per 10,000 person-years.

翻译：边缘群体暴露于不成比例的高水平空气污染中。在此背景下，对空气污染监管政策健康效应的异质性进行稳健评估，是论证和设计最大化保护性未来干预措施的关键。此类评估受两个关键问题困扰：1）许多空气污染监管政策聚焦于大规模排放源的干预，而由此产生的健康效应却在暴露人群中测量；2）由于空气污染传输，对一个排放源的干预可能影响地理上相距较远的社区。在因果推断中，此类情景被描述为二部图网络干扰（BNI）。据我们所知，目前尚无文献考虑如何在BNI下估计异质性因果效应。首先，我们提出、实现并评估了在BNI背景下通过增强逆概率加权和G计算方法估计亚组特异性治疗效应的因果估计量。其次，我们设计并实现了一种用于BNI的经验蒙特卡洛模拟方法，通过该方法评估提出估计量的性能。第三，我们使用提出方法估计燃煤电厂烟气脱硫洗涤器安装对居住在美国29,034个邮政编码地区的27,312,190名医疗保险受益人中缺血性心脏病住院的因果效应。虽然我们发现洗涤器在整个人群中无统计学显著效应，但在边缘群体中发现了保护效应。对于高贫困且以非白人为主的邮政编码地区，当其最具影响力的发电厂安装洗涤器而影响力较低的工厂未处理时，发现缺血性心脏病住院率出现统计学显著下降，降幅为每万人年减少6.4至43.1例住院。