Generalized propensity score approach to causal inference with spatial interference

TL;DR

This paper introduces a new causal inference framework for spatial data with interference, using a generalized propensity score and Bayesian spline models to estimate direct and spill-over effects.

Contribution

It develops a novel causal approach that accounts for spatial interference and proximity effects, extending traditional methods to spatially correlated treatments.

Findings

Accurate estimation of causal effects in spatial interference scenarios.

Bayesian spline model effectively reduces dimensionality and improves inference.

Application to wildland fires and air pollution demonstrates practical utility.

Abstract

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.