Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching

Abstract

Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units 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 some forms of 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.

Causal Inference With Interfering Units for Cluster and Population Level Treatment Allocation Programs

“…While work in Papadogeorgou et al. () corroborated this effectiveness of SCR and SNCR for reducing NO x emissions, the analysis of ambient ozone pollution in that paper ignores the possibility of interference and estimates an effect on ozone very close to zero. However, interference is a key component in the study of air pollution: ambient pollution concentrations near a power plant will depend on the treatment levels of other nearby power plants.…”

“…A preliminary investigation of these same data in Papadogeorgou et al. () ignored interference and indicated that these systems causally reduced NO x emissions (an important precursor to ozone pollution) but estimated an effect on ambient ozone very close to zero. The analysis here to address the possibility of interference produces meaningfully different results that are more consistent with the literature relating NO x emissions to ambient ozone pollution.…”

Testing for Network and Spatial Autocorrelation

“…We applied cutoffs based on the (0.25, 0.5, 0.75) quantiles of each simulated dataset to convert We also simulated data under a spatially correlated error model (F Dormann et al, 2007), using a continuous weight matrix estimated from real spatial data. We used the longitude and latitude of 473 U.S. power generating facilities (Papadogeorgou, 2017;Papadogeorgou et al, 2016) to construct a Euclidean distance matrix D = [d ij ], where d ij is the Euclidean distance between facilities i and j, based on which we constructed a weight matrix Π = [π ij ] where π ij = exp (−qd ij /max({d ij : i, j = 1, 2, . .…”

“…In addition to the locations of the 473 facilities, the data includes information on the characteristics of the surrounding geographic areas. Details can be found in Table G.1 in Papadogeorgou et al (2016).…”

Selecting a Scale for Spatial Confounding Adjustment

“…In a causal inference framework, confounding due to geography has been recently addressed using spatial propensity scores (Papadogeorgou et al, 2019;Davis et al, 2019), however in those settings the exposure of interest was not explicitly spatial.…”