The growing interest in causal inference in recent years has led to new causal inference
methodologies and their applications across disciplines and research domains. Yet, studies …

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 …

The concept of spatial confounding is closely connected to spatial regression, although no
general definition has been established. A generally accepted idea of spatial confounding in …

Statistical methods to evaluate the effectiveness of interventions are increasingly challenged
by the inherent interconnectedness of units. Specifically, a recent flurry of methods research …

Interference arises when an individual's potential outcome depends on the individual
treatment level, but also on the treatment level of others. A common assumption in the …

This manuscript bridges the divide between causal inference and spatial statistics,
presenting novel insights for causal inference in spatial data analysis, and establishing how …

In the last two decades, considerable research has been devoted to a phenomenon known
as spatial confounding. Spatial confounding is thought to occur when there is collinearity …

Estimating the causal effects of a spatially-varying intervention on a spatially-varying
outcome may be subject to non-local confounding (NLC), a phenomenon that can bias …

Assessing associations between a response of interest and a set of covariates in spatial
areal models is the leitmotiv of ecological regression. However, the presence of spatially …

Supplement to “Mitigating unobserved spatial confounding when estimating the effect of
supermarket access on cardiovascular disease deaths”.(Schnell and Papadogeorgou …