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Thursday, December 19, 2019

Conferencia INAMAT: Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching

by Georgia Papadogeorgou

Fecha: 19 de diciembre a las 9:30

Lugar: Sala de Tercer Ciclo del Departamento de Estadística, Informática y Matemáticas. (Edificio de los Magnolios, 1ª Planta)

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.


Breve CV: Georgia Papadogeorgou is currently a postdoctoral associate at the Department of Statistical Science at Duke University working with professors David Dunson and Fan Li. Previously, she completed her Ph.D. in Biostatistics at Harvard University under the supervision of professors Francesca Dominici and Cory Zigler. Her primary focus is causal inference methodology with structured data including spatial data, but she has also worked on Bayesian methodology for high dimensional data.


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