A review of spatial causal inference methods for environmental and epidemiological applications

TL;DR

This paper reviews spatial causal inference methods in environmental and epidemiological research, highlighting challenges, current approaches, and future directions, with practical examples and code implementations.

Contribution

It provides a comprehensive overview of spatial causal inference techniques, including unmeasured confounding, spatial interference, and spatiotemporal analysis, with comparisons and practical applications.

Findings

Methods exploiting spatial structure help address unmeasured confounding

Approaches to spatial interference include common simplifying assumptions

Application to air pollution and COVID-19 mortality demonstrates method utility

Abstract

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 casual 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.