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 (CVAE) with a spatial prior, then estimates causal effects via a flexible outcome model. We show that this approach enables nonparametric identification of both direct and spillover effects under weak assumptions--without requiring multiple treatment types or a known model of the latent field. 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 datasets in environmental health and social science. By turning interference into a multi-cause signal, our framework bridges spatial and deconfounding literatures to advance robust causal inference in structured data.

翻译：空间领域的因果推断面临两个相互交织的挑战：(1) 未观测的空间混杂因素（如天气、空气污染或流动性）会同时影响处理变量与结果变量；(2) 邻近处理产生的干扰会违背标准的无干扰假设。现有方法通常通过假设其中一方不存在来处理另一方，而我们证明这两者存在深刻联系：干扰揭示了潜在混杂因子的结构。基于这一洞见，我们提出空间去混杂器——一种两阶段方法：首先利用带有空间先验的条件变分自编码器（CVAE）从局部处理向量中重构替代混杂因子，随后通过灵活的结果模型估计因果效应。我们证明，该方法能够在较弱假设下非参数地识别直接效应与溢出效应，且无需多种处理类型或已知的潜在场模型。在实证研究中，我们扩展了空间混杂基准测试套件SpaCE，使其包含处理干扰，并表明空间去混杂器在环境健康与社会科学领域的真实数据集上持续提升效应估计的准确性。通过将干扰转化为多因信号，本框架连接了空间分析与去混杂研究领域，推动结构化数据中稳健因果推断的发展。