Estimating the causal effects of a spatially-varying intervention on a
spatially-varying outcome may be subject to non-local confounding (NLC), a
phenomenon that can bias estimates when the treatments and outcomes of a given
unit are dictated in part by the covariates of other nearby units. In
particular, NLC is a challenge for evaluating the effects of environmental
policies and climate events on health-related outcomes such as air pollution
exposure. This paper first formalizes NLC using the potential outcomes
framework, providing a comparison with the related phenomenon of causal
interference. Then, it proposes a broadly applicable framework, termed
"weather2vec", that uses the theory of balancing scores to learn
representations of non-local information into a scalar or vector defined for
each observational unit, which is subsequently used to adjust for confounding
in conjunction with causal inference methods. The framework is evaluated in a
simulation study and two case studies on air pollution where the weather is an
(inherently regional) known confounder