2018
DOI: 10.1093/biostatistics/kxx074
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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 i… Show more

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Cited by 46 publications
(78 citation statements)
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“…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.…”
Section: Evaluating the Effectiveness Of Power Plant Emissions Contromentioning
confidence: 91%
See 1 more Smart Citation
“…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.…”
Section: Evaluating the Effectiveness Of Power Plant Emissions Contromentioning
confidence: 91%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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, . .…”
Section: Testing For Spatial Autocorrelation In Categorical Variablesmentioning
confidence: 99%
“…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).…”
Section: Spatial Datamentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%