  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. Lastly, we use the proposed methodology to study the health
effects of ambient air pollution on mortality among Medicare enrollees in the
United States.
