Quantile-based causal inference for spatio-temporal processes: Assessing the impacts of wildfires on US air quality

TL;DR

This paper introduces a quantile-based causal inference model to analyze how wildfires affect air quality across different pollution levels, revealing that impacts are most significant during extreme pollution events in specific US regions.

Contribution

The paper develops a novel quantile-based causal inference method for spatio-temporal data, addressing unmeasured confounders and heterogeneity in wildfire impacts on air quality.

Findings

Wildfires have significant causal effects on high pollution quantiles in Western US states.

Impacts are negligible at lower pollution levels, highlighting heterogeneity.

The method outperforms mean-based approaches in bias correction and inference accuracy.

Abstract

Wildfires pose an increasingly severe threat to air quality, yet quantifying their causal impact remains challenging due to unmeasured meteorological and geographic confounders. Moreover, wildfire impacts on air quality may exhibit heterogeneous effects across pollution levels, which conventional mean-based causal methods fail to capture. To address these challenges, we develop a Quantile-based Latent Spatial Confounder Model (QLSCM) that substitutes conditional expectations with conditional quantiles, enabling causal analysis across the entire outcome distribution. We establish the causal interpretation of QLSCM theoretically, prove the identifiability of causal effects, and demonstrate estimator consistency under mild conditions. Simulations confirm the bias correction capability and the advantage of quantile-based inference over mean-based approaches. Applying our method to contiguous US wildfire and air quality data, we uncover important heterogeneous effects: fire radiative power exerts significant positive causal effects on aerosol optical depth at high quantiles in Western states like California and Oregon, while insignificant at lower quantiles. This indicates that wildfire impacts on air quality primarily manifest during extreme pollution events. Regional analyses reveal that Western and Northwestern regions experience the strongest causal effects during such extremes. These findings provide critical insights for environmental policy by identifying where and when mitigation efforts would be most effective.