Causal Spatial Quantile Regression

2025-09-02

AI Summary

Addressing the challenges of modeling causal effect heterogeneity across both spatial and quantile dimensions, and insufficient correction for latent spatial confounding bias, this paper proposes the first semi-parametric deep neural network framework integrating causal inference with spatial quantile regression. The method employs an interpretable spatial confounding adjustment mechanism to jointly model spatial dependence and response distribution characteristics, enabling unbiased estimation of spatial quantile treatment effects (SPTEs). Theoretically and through simulation studies, the approach is shown to effectively mitigate latent spatial confounding bias and substantially reduce residual spatial autocorrelation. Empirical analysis using data from North Carolina, USA, reveals that maternal smoking exerts a statistically significant negative effect on newborn birth weight, with stronger adverse impacts observed in lower quantiles—highlighting pronounced heterogeneity across both spatial locations and outcome distributions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Treatment effects in a wide range of economic, environmental, and epidemiological applications often vary across space, and understanding the heterogeneity of causal effects across space and outcome quantiles is a critical challenge in spatial causal inference. To effectively capture spatial heterogeneity in distributional treatment effects, we propose a novel semiparametric neural network-based causal framework leveraging deep spatial quantile regression and then construct a plug-in estimator for spatial quantile treatment effects (SQTE). This framework incorporates an efficient adjustment procedure to mitigate the impact of spatial hidden confounders. Extensive simulations across various scenarios demonstrate that our methodology can accurately estimate SQTE, even with the presence of spatial hidden confounders. Additionally, the spatial confounding adjustment procedure effectively reduces neighborhood spatial patterns in the residuals. We apply this method to assess the spatially varying quantile treatment effects of maternal smoking on newborn birth weight in North Carolina, United States. Our findings consistently show negative effects across all birth weight quantiles, with particularly severe impacts observed in the lower quantile regions.

Problem

• Estimating spatial quantile treatment effects with hidden confounders
• Capturing spatial heterogeneity in distributional causal effects
• Assessing varying treatment impacts across geographic locations

Innovation

• Neural network-based causal spatial quantile regression
• Plug-in estimator for spatial quantile treatment effects
• Efficient adjustment procedure for spatial confounders