Spatially varying effect estimates of mediation in the presence of spatial auto-correlation
- 1Scripps Institution of Oceanography, University of California San Diego
Abstract
The use of mediation analysis is common in epidemiology to decompose an exposure-outcome total effect into direct and indirect effects via a given set of mediators. The counterfactual framework in causal inference allowed for great improvements in mediation analysis to define identification assumptions and clarify sources of bias in different settings. Multiple mediation methods have been proposed to consider confounders of various types and for different settings such as longitudinal designs. However, in some settings we may reasonably expect spatially heterogeneous estimates of both natural direct and indirect effects. In various research areas, especially environmental and social epidemiology, we would expect there to be spatial dependence that could be leveraged. In this paper, we describe and compare a set of approaches to perform spatial mediation analyses and conduct an extensive set of simulations to quantify their performance. We also present a case study assessing the spatially heterogeneous mediating role of PM2.5 in the association between income and cardiovascular diseases. We provide all code via GitHub for replication and reproducibility purposes. We find that using a framework combining geographically weighted regression (GWR) and other spatially varying coefficient (SVC) models and the product method of mediation analysis is accurate and precise at estimating spatially heterogeneous effect estimates and handling spatial confounding in a grid setting. Such spatial approaches can be extended to any mediation approach and various confounding structures.
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BibTeX
@misc{hansen2026spatially,
title = {{Spatially varying effect estimates of mediation in the presence of spatial auto-correlation}},
author = {K. Hansen and Y. Ma and A. Schwartzman and T. Benmarhnia},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.30.26366937},
url = {https://www.medrxiv.org/content/10.64898/2026.10.30.26366937v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Spatially varying effect estimates of mediation in the presence of spatial auto-correlation
AU - K. Hansen
AU - Y. Ma
AU - A. Schwartzman
AU - T. Benmarhnia
PY - 2026
DA - 2026/10/30
PB - medRxiv
DO - 10.64898/2026.10.30.26366937
UR - https://www.medrxiv.org/content/10.64898/2026.10.30.26366937v1
AB - The use of mediation analysis is common in epidemiology to decompose an exposure-outcome total effect into direct and indirect effects via a given set of mediators. The counterfactual framework in causal inference allowed for great improvements in mediation analysis to define identification assumptions and clarify sources of bias in different settings. Multiple mediation methods have been proposed to consider confounders of various types and for different settings such as longitudinal designs. However, in some settings we may reasonably expect spatially heterogeneous estimates of both natural direct and indirect effects. In various research areas, especially environmental and social epidemiology, we would expect there to be spatial dependence that could be leveraged. In this paper, we describe and compare a set of approaches to perform spatial mediation analyses and conduct an extensive set of simulations to quantify their performance. We also present a case study assessing the spatially heterogeneous mediating role of PM2.5 in the association between income and cardiovascular diseases. We provide all code via GitHub for replication and reproducibility purposes. We find that using a framework combining geographically weighted regression (GWR) and other spatially varying coefficient (SVC) models and the product method of mediation analysis is accurate and precise at estimating spatially heterogeneous effect estimates and handling spatial confounding in a grid setting. Such spatial approaches can be extended to any mediation approach and various confounding structures.
ER -