利用邻近处理干扰揭示隐含空间混杂，提升空间因果推断准确性。
Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
- 通过条件变分自编码器从局部处理向量重建替代混杂因子
- 在弱假设下实现直接与溢出效应的非参数识别
- 适用于环境健康与社会科学中的真实空间数据
空间因果推断面临两大相互交织的挑战：（1）未测量的空间因素（如天气、空气污染或流动）导致处理与结果混杂；（2）邻近处理间的干扰违反标准无干扰假设。现有方法通常分别处理其中一者，忽略另一者。本文发现二者深度关联：干扰可揭示潜在混杂结构。基于此，提出Spatial Deconfounder，一种两阶段方法：首先使用带空间先验的条件变分自编码器（C-VAE），从局部处理向量重构替代混杂因子；随后采用灵活的结果模型估计因果效应。该方法在弱假设下实现了直接效应与溢出效应的非参数识别，无需多种处理类型或已知潜变量场模型。实证上，将SpaCE基准套件扩展以包含处理干扰，结果显示Spatial Deconfounder在真实世界环境健康与社会科学数据集上持续提升效应估计精度。通过将局部干扰转化为潜在空间混杂的多因代理，本框架推动了空间数据因果推断的稳健性。
原文摘要 · Abstract (English)
Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions. While existing methods typically address one by assuming away the other, we show they are deeply connected: interference reveals structure in the latent confounder. Leveraging this insight, we propose the Spatial Deconfounder, a two-stage method that reconstructs a substitute confounder from local treatment vectors using a conditional variational autoencoder (C-VAE) with a spatial prior, then estimates causal effects with a flexible outcome model. We show that this enables nonparametric identification of direct and spillover effects under weak assumptions--without multiple treatment types or a known latent-field model. Empirically, we extend SpaCE, a benchmark suite for spatial confounding, to include treatment interference, and show that the Spatial Deconfounder consistently improves effect estimation across real-world environmental health and social science datasets. By turning local interference into a multi-cause proxy for latent spatial confounding, our framework advances robust causal inference for spatial data.
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