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

2025-12-18

AI Summary

Assessing the causal impact of wildfires on U.S. air quality is hindered by unmeasured meteorological and geographical confounders and heterogeneous treatment effects, rendering conventional mean regression inadequate for capturing nonlinear impacts under extreme pollution. To address this, we propose the Quantile Latent Space Confounding Model (QLSCM), the first spatial causal identification framework grounded in quantile regression—replacing conditional expectations with conditional quantiles to enable full-distribution causal inference. We establish theoretical identifiability and estimation consistency under mild regularity conditions. Empirically, applying QLSCM to satellite-derived wildfire radiative power and aerosol optical depth data reveals that wildfire effects are concentrated at upper quantiles (e.g., above the 90th percentile), particularly in western states including California and Oregon. These findings provide a novel paradigm and empirical foundation for designing spatiotemporally targeted environmental policies.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Develops a quantile-based model to assess wildfire impacts on air quality
• Addresses unmeasured confounders and heterogeneous effects across pollution levels
• Identifies regional and extreme-event-specific causal effects for policy insights

Innovation

• Quantile-based model for spatio-temporal causal inference
• Substitutes conditional expectations with conditional quantiles
• Enables causal analysis across entire outcome distribution