We develop new methodology to improve our understanding of the causal effects of multivariate air pollution exposures on public health. Typically, exposure to air pollution for an individual is measured at their home geographic region, though people travel to different regions with potentially different levels of air pollution. To account for this, we incorporate estimates of the mobility of individuals from cell phone mobility data to get an improved estimate of their exposure to air pollution. We treat this as an interference problem, where individuals in one geographic region can be affected by exposures in other regions due to mobility into those areas. We propose policy-relevant estimands and derive expressions showing the extent of bias one would obtain by ignoring this mobility. We additionally highlight the benefits of the proposed interference framework relative to a measurement error framework for accounting for mobility. We develop novel estimation strategies to estimate causal effects that account for this spatial spillover utilizing flexible Bayesian methodology. Empirically we find that this leads to improved estimation of the causal effects of air pollution exposures over analyses that ignore spatial spillover caused by mobility.

翻译：我们开发了一种新方法，以增进对多变量空气污染暴露对公共健康因果效应的理解。通常，个体空气污染暴露是在其居住地理区域测量的，但人们会前往空气污染水平可能不同的区域。为考虑这一点，我们利用手机移动数据估算个体移动性，以改进对其空气污染暴露的估算。我们将其视为一个干扰问题——即某一地理区域的个体可能因移动进入其他区域而受到该区域暴露的影响。我们提出政策相关的估计量，并推导出忽略移动性时可能产生的偏倚程度的表达式。此外，我们强调了所提出的干扰框架相较于测量误差框架在考虑移动性方面的优势。我们利用灵活的贝叶斯方法开发了新型估计策略，以估算考虑这种空间溢出效应的因果效应。实证表明，相较于忽略由移动性导致的空间溢出效应的分析，该方法能更准确地估算空气污染暴露的因果效应。