Keywords: propensity scores, environmental policy, spatially-indexed data, unobserved confounding
Many observational research settings are confronted with spatially-indexed data where the relative locations of the observations may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed Distance Adjusted Propensity Score Matching (DAPSm) that incorporates information on units’ spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a “standard” propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade offs of prioritizing observed confounding adjustment versus spatial proximity adjustment.