Spatial Confounding: A review of concepts, challenges, and current approaches

TL;DR

This paper reviews the concept of spatial confounding, its challenges, and current solutions, providing a unified perspective and empirical comparisons to guide future research in spatial statistics.

Contribution

It offers a comprehensive review of definitions, models, and recent methodological advances in addressing spatial confounding, including empirical evaluations and future directions.

Findings

Unified view of approaches for areal and geostatistical data

Empirical comparison of methods on real datasets

Discussion of theoretical and practical merits

Abstract

Spatial confounding is a persistent challenge in spatial statistics, influencing the validity of statistical inference in models that analyze spatially-structured data. The concept has been interpreted in various ways but is broadly defined as bias in estimates arising from unmeasured spatial variation. In this paper we review definitions, classical spatial models, and recent methodological advances, including approaches from spatial statistics and causal inference. We provide an unified view of the many available approaches for areal as well as geostatistical data and discuss their relative merits both theoretically and empirically with a head-to-head comparison on real datasets. Finally, we leverage the results of the empirical comparisons to discuss directions for future research.