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Activity Number: 226 - Causal Inference with Spatial Environmental Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Royal Statistical Society
Abstract #306860 Presentation
Title: Causal Inference and Casual Spatial Models: The Importance of Modeling Mechanism in Spatial Data
Author(s): Ephraim Hanks*
Companies: Pennsylvania State University
Keywords: Spatial models; Causal modeling; Interference

Spatial autocorrelation models are critical tools in the analysis of spatial environmental, epidemiological, and ecological data. While the most common spatial models, such as Matern or CAR models, have well-established utility for prediction and smoothing, the interpretation of standard spatial models is rarely straightforward. We first consider the Rubin causal model applied to spatial statistics, and illustrate how standard spatial models are unable to directly answer causal questions that are of interest in common situations. We then present a general class of spatial models that directly model scientific mechanisms underlying common processes in environmental health and ecology. This class of models contains common Matern, CAR, and SAR models as special cases, and allows for causal modeling of spatial data in scenarios where the scientific mechanism underlying spatial autocorrelation is well-understood.

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

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