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Activity Number: 21 - Bayesian Disease Mapping and Spatial Epidemiology: New Directions and New Frontiers
Type: Topic-Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #317123
Title: Generalized Propensity Score Approach to Causal Inference with Spatial Interference
Author(s): Andrew Giffin* and Brian Reich and Shu Yang and Ana Rappold
Companies: North Carolina State University and North Carolina State University and North Carolina State University and Environmental Protection Agency
Keywords: Air pollution; Causal inference; Interference; Spatial process; Wildfire

Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial interference, taking into account that treatments at nearby locations are more influential than treatments at locations further apart. Under the no unmeasured confounding assumption, we show that a generalized propensity score is sufficient to remove all measured confounding. To reduce dimensionality issues, we propose a Bayesian spline-based regression model accounting for a sufficient set of variables for the generalized propensity score. A simulation study demonstrates the accuracy and coverage properties. We apply the method to estimate the causal effect of wildland fires on air pollution in the Western United States over 2005--2018.

Authors who are presenting talks have a * after their name.

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