# Generalized propensity score approach to causal inference with spatial interference

arXiv: https://arxiv.org/abs/2007.00106 · HTML: https://mlsanity.com/paper/2007.00106/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments](https://mlsanity.com/paper/2607.05155/) (2026)
- [Learning Individual Treatment Effects under Heterogeneous Interference in Networks](https://mlsanity.com/paper/2210.14080/) (2022)
- [Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference](https://mlsanity.com/paper/2405.08174/) (2024)
- [Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework](https://mlsanity.com/paper/2602.03459/) (2026)
- [Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference](https://mlsanity.com/paper/2608.01352/) (2026)
- [GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation](https://mlsanity.com/paper/2609.28895/) (2026)
- [Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2609.36046/) (2026)

## Abstract (arXiv)

Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial interference, taking into account that treatments at nearby locations are more influential than treatments at locations further apart. Under the no unmeasured confounding assumption, we show that a generalized propensity score is sufficient to remove all measured confounding. To reduce dimensionality issues, we propose a Bayesian spline-based regression model accounting for a sufficient set of variables for the generalized propensity score. A simulation study demonstrates the accuracy and coverage properties. We apply the method to estimate the causal effect of wildland fires on air pollution in the Western United States over 2005--2018.