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Propensity score matching is a common tool for adjusting for observedconfounding in observational studies, but is known to have limitations in thepresence of unmeasured confounding. In many settings, researchers areconfronted with spatially-indexed data where the relative locations of theobservational units may serve as a useful proxy for unmeasured confounding thatvaries according to a spatial pattern. We develop a new method, termed DistanceAdjusted Propensity Score Matching (DAPSm) that incorporates information onunits' spatial proximity into a propensity score matching procedure. We showthat DAPSm can adjust for both observed and some forms of unobservedconfounding and evaluate its performance relative to several other reasonablealternatives for incorporating spatial information into propensity scoreadjustment. The method is motivated by and applied to a comparativeeffectiveness investigation of power plant emission reduction technologiesdesigned to reduce population exposure to ambient ozone pollution. Ultimately,DAPSm provides a framework for augmenting a "standard" propensity scoreanalysis with information on spatial proximity and provides a transparent andprincipled way to assess the relative trade offs of prioritizing observedconfounding adjustment versus spatial proximity adjustment.

Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching