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

A spatial interference approach to account for mobility in air pollution studies with multivariate continuous treatments