Robust Spatial Confounding Adjustment via Basis Voting
通过基函数投票稳健调整空间混杂AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究未测空间混杂下的因果效应识别与估计，以暴露和混杂的基函数支持差异作为识别依据。
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通过基函数投票稳健调整空间混杂
- 研究问题
- 当空间暴露与未测混杂都较平滑，不能依赖暴露具有更高频变化时，能否利用两者基函数支持的差异识别因果效应？
- 方法与思路
- 作者提出基函数投票，按各工作基函数构造候选估计并取众数，以相对多数规则识别效应，同时设计较高效的候选估计量。
- 材料陈述的贡献
- 摘要称，该框架放松暴露高频变化要求，无需预先知道有效基函数；理论、模拟及应用支持其在满足基支持分离条件时恢复效应。
- 阅读关注点
- 应核对有效基函数的相对多数条件、基展开与众数估计方式、候选估计量效率比较，以及有限样本中支持分离不足的后果。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收状态未核实；识别仍依赖暴露与混杂的基支持差异，摘要未说明完整条件或如何验证这些差异。
原文摘要
Estimating 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 effect estimation in spatial regression models 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 design this estimator as the mode of several candidate estimators each computed based on a single working basis function. We also show that the standard projection-based candidate estimator typically used in other plurality-rule based methods is inefficient, and provide a more efficient novel candidate. 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. 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.