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

arXiv: https://arxiv.org/abs/2007.02714 · HTML: https://mlsanity.com/paper/2007.02714/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Policy Evaluation for Temporal and/or Spatial Dependent Experiments](https://mlsanity.com/paper/2202.10887/) (2022)
- [Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities](https://mlsanity.com/paper/2301.12985/) (2023)
- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
- [Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach](https://mlsanity.com/paper/2505.20130/) (2025)
- [A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets](https://mlsanity.com/paper/2202.10574/) (2022)
- [Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa](https://mlsanity.com/paper/2206.06410/) (2022)
- [Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference](https://mlsanity.com/paper/2405.08174/) (2024)
- [Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences](https://mlsanity.com/paper/2407.17910/) (2024)
- [Spatio-Temporal Hierarchical Causal Models](https://mlsanity.com/paper/2511.20558/) (2025)
- [The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection](https://mlsanity.com/paper/2312.06616/) (2023)
- [Distributed Dynamic Invariant Causal Prediction in Environmental Time Series](https://mlsanity.com/paper/2603.02902/) (2026)
- [Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2609.36046/) (2026)

## Abstract (arXiv)

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.