Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and non-local treatment. This is especially relevant when estimating the effect of power plant emissions controls on population health, as pollution exposure is dictated by (i) the location of point-source emissions, as well as (ii) the transport of pollutants across space via dynamic physical-chemical processes. In this work, we estimate the effectiveness of air quality interventions at coal-fired power plants in reducing two adverse health outcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-cause mortality. We develop methods for causal inference with interference when the underlying network structure is not known with certainty and instead must be estimated from ancillary data. We offer a Bayesian, spatial mechanistic model for the interference mapping which we combine with a flexible non-parametric outcome model to marginalize estimates of causal effects over uncertainty in the structure of interference. Our analysis finds some evidence that emissions controls at upwind power plants reduce asthma ED visits and all-cause mortality, however accounting for uncertainty in the interference renders the results largely inconclusive.

翻译：基于空间环境数据的因果推断常因存在干扰而具有挑战性：观测单元的结果取决于本地与非本地处理的某种组合。这在评估电厂排放控制对人群健康的影响时尤为突出，因为污染暴露既受（i）点源排放位置的影响，也受（ii）污染物通过动态物理化学过程在空间输运的影响。本研究估算了2016年德克萨斯州燃煤电厂空气污染干预措施在降低两种健康不良结局方面的有效性：儿童哮喘急诊就诊量与老年医疗保险全因死亡率。我们开发了一种在底层网络结构非确切已知（需从辅助数据估计）情况下的干扰因果推断方法，提出了一种结合空间机制建模的贝叶斯干扰映射模型，并融合灵活的非参数结局模型，以消除干扰结构不确定性对因果效应估计的边际影响。分析结果提示，上风向电厂排放控制可降低哮喘急诊就诊率与全因死亡率，但考虑干扰的不确定性后，结论呈现显著不确定性。