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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 #301751 Presentation
Title: Causal Spatial Analysis in the Presence of Unmeasured Confounders
Author(s): Brian Reich* and Shu Yang and Yawen Guan
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Keywords: Causal inference; Spatial data; Confounders; Matern; Bayesian

Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we begin by formalizing spatial regression using counterfactual outcomes and derive necessary conditions on the coherence between the covariate of interest and the unmeasured confounders that ensure the causal effect of the covariate is estimable. We propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matern coherence function to more robust semiparametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets.

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

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