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A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
Author(s) -
Reich Brian J.,
Yang Shu,
Guan Yawen,
Giffin Andrew B.,
Miller Matthew J.,
Rappold Ana
Publication year - 2021
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12452
Subject(s) - causal inference , computer science , inference , observational study , context (archaeology) , confounding , econometrics , environmental epidemiology , spatial correlation , contrast (vision) , spatial analysis , spatial epidemiology , causality (physics) , bayesian inference , bayesian probability , data mining , statistics , mathematics , artificial intelligence , geography , epidemiology , medicine , environmental health , telecommunications , physics , archaeology , quantum mechanics
Summary The scientific rigor and computational methods of causal inference have had great impacts on many disciplines but have only recently begun to take hold in spatial applications. Spatial causal inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID‐19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.