论文标题
空间混淆的光谱调整
A spectral adjustment for spatial confounding
论文作者
论文摘要
调整未衡量的混杂因素通常是一个棘手的问题，但是在空间环境中，在某些条件下可能是可能的。在本文中，我们得出了有关感兴趣的治疗变量与未衡量的混杂因子之间的一致性的必要条件，以确保治疗的因果效应。我们在光谱域中指定我们的模型和假设，以允许在不同的空间分辨率下进行不同程度的混淆。确保可识别性的关键假设是，在全球尺度上存在混淆在本地尺度上消失了。我们表明，光谱域中的这个假设等于通过在响应变量的平均值中添加空间平滑的处理变量的空间平滑版本，以调整空间域中的全局尺度混杂。在此一般框架中，我们提出了一系列混杂调整方法，范围从基于Matern Cooherence函数的参数调整到使用平滑光谱的更健壮的半参数方法。这些想法适用于模拟和真实数据集的Areal和地理数据
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we derive necessary conditions on the coherence between the treatment variable of interest and the unmeasured confounder that ensure the causal effect of the treatment is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. The key assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the treatment variable to the mean of the response variable. Within this general framework, we propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matern coherence function to more robust semi-parametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets
