ArticleInternational statistical review = Revue internationale de statistique2021
A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.
Article in International statistical review = Revue internationale de statistique, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Research opportunities to advance cardiovascular health through a planetary health lens.American journal of preventive cardiology · 2026Review
- Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.Statistics in medicine · 2026Article
- Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approach.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Mitigating the risk of bias exacerbation when controlling for unmeasured spatial confounding for binary exposures.American journal of epidemiology · 2026Article
- Impacts of Social Environments on Neighborhood Depression Incidence: Fully Accounting for Spatial Effects.International journal of environmental research and public health · 2026Article
- Spatial Patterns of Breast Cancer Risk Associated with Industrial and Environmental Pollutants: A Scoping Review.Toxics · 2026Review
- Article
- Spatial spillover effects of area-level socioeconomic factors on life expectancy in Japan: an ecological study.International journal of health geographics · 2025Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Causal inference unveils how forest coverage mitigates excess snakebite cases during rainfall seasons in Colombia.Scientific reports · 2025Article
- Examining associations between street vegetation and child firearm violence at microgeographic locations in 3 US cities: a case-control study.BMC pediatrics · 2025Article
- Incorporating small-area estimation into mediation analyses with areal datasets.Spatial and spatio-temporal epidemiology · 2025Article
- Empirical methods that provide physical descriptions of dynamic cellular processes.Biophysical journal · 2025Review
- A research agenda for GIScience in a time of disruptions.International journal of geographical information science : IJGIS · 2025Article
- CAUSAL HEALTH IMPACTS OF POWER PLANT EMISSION CONTROLS UNDER MODELED AND UNCERTAIN PHYSICAL PROCESS INTERFERENCE.The annals of applied statistics · 2024Article
- Estimating spatially varying health effects of wildland fire smoke using mobile health data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next?Environmental science & technology · 2024Review
- Remotely sensing potential climate change tipping points across scales.Nature communications · 2024Review
- Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference.Scientific reports · 2024Article
- Alleviating spatial confounding in frailty models.Biostatistics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
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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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.