A0994
Title: An improved doubly robust estimator using partially recovered unmeasured spatial confounder
Authors: Sayli Pokal - University of Nebraska Lincoln (United States)
Yawen Guan - University of Nebraska Lincoln (United States)
Honglang Wang - Indiana University-Purdue University Indianapolis (United States)
Yuzhen Zhou - University of Nebraska-Lincoln (United States)
Yuzhen Zhou - University of Nebraska Lincoln (United States) [presenting]
Abstract: Studies in environmental and epidemiological sciences are often spatially varying and observational in nature with the aim of establishing cause and effect relationships. One of the major challenges with such studies is the presence of unmeasured confounders. Spatial confounding is the phenomenon in which the spatial residuals are correlated to the spatial covariates in the model. While there is extensive literature studying the effect of spatial confounding bias in the case of continuous covariates, not much work has been done in scenarios where the covariate of interest is binary. A novel method is developed which adjusts for the spatial confounding bias under a spatial-causal inference framework when the covariate of interest is binary. By combining tools from spatial statistics and causal inference literature we propose a method that reduces the bias due to spatial confounding. Through simulation studies, we demonstrate that the proposed improved doubly robust estimator outperforms the existing methods and has the lowest bias and close to nominal coverage in most scenarios. Finally, we implement our method to estimate the effect of installing SCR/SNCR NOx emission control technologies on ambient ozone concentrations.
