标题： 通过基投票进行鲁棒的空间混杂调整 显示英文标题

标题： Robust Spatial Confounding Adjustment via Basis Voting

摘要： 估计空间结构化暴露的因果效应受到未测量的空间混杂因素的困扰，这会削弱空间线性回归模型中的可识别性，除非施加结构假设。 我们开发了一个一般的因果效应估计框架，放松了通常假设的暴露包含比混杂因素更高频率变化的要求。 我们提出了基投票，这是一种在空间文献中新颖的多数规则估计器，只有在暴露和混杂因素的空间基展开中，暴露的支持存在多个基函数而混杂因素不支持时，才能一致地识别因果效应。 这一假设推广了在空间混杂下用于因果效应识别的现有差异基支持假设，并不要求事先知道哪些基函数满足此支持条件。 我们还表明，其他依赖差异支持的方法中使用的标准投影估计器是低效的，并提供了一个更高效的新型估计器。 大量模拟和一个实际应用表明，当暴露和混杂信号在多数基函数上可分离时，我们的方法能够可靠地恢复无偏的因果估计。 重要的是，由于不依赖于更高频率的变化，我们的方法在暴露是平滑空间函数的情况下仍然适用，例如到污染源或主要道路的距离，在环境研究中很常见。 显示英文摘要

摘要： Estimating causal effects of spatially structured exposures is complicated by unmeasured spatial confounders, which undermine identifiability in spatial linear regression models unless structural assumptions are imposed. We develop a general framework for causal effect estimation that relaxes the commonly assumed requirement that exposures contain higher-frequency variation than confounders. We propose basis voting, a plurality-rule estimator - novel in the spatial literature - that consistently identifies causal effects only under the assumption that, in a spatial basis expansion of the exposure and confounder, there exist several basis functions in the support of the exposure but not the confounder. This assumption generalizes existing assumptions of differential basis support used for identification of the causal effect under spatial confounding, and does not require prior knowledge of which basis functions satisfy this support condition. We also show that the standard projection-based estimator used in other methods relying on differential support is inefficient, and provide a more efficient novel estimator. Extensive simulations and a real-world application demonstrate that our approach reliably recovers unbiased causal estimates whenever exposure and confounder signals are separable on a plurality of basis functions. Importantly, by not relying on higher-frequency variation, our method remains applicable to settings where exposures are smooth spatial functions, such as distance to pollution sources or major roadways, common in environmental studies.