论文标题
普遍的倾向得分方法方法用于因空间干扰的因果推断
Generalized propensity score approach to causal inference with spatial interference
论文作者
论文摘要
许多空间现象表现出治疗干扰，其中一个位置的处理可能会影响其他位置的反应。由于干扰违反了稳定的单位治疗价值假设，因此不适用于因果推理的标准方法。我们提出了一个新的因果框架，以在存在空间干扰的情况下恢复直接和溢出效应，考虑到附近位置的治疗比进一步分开的治疗更具影响力。在没有未衡量的混杂假设下，我们表明，广义倾向得分足以消除所有测量的混杂。为了减少维数问题，我们提出了一个基于贝叶斯的基于贝叶斯样条的回归模型，该模型占广义倾向得分的足够变量。一项模拟研究证明了准确性和覆盖率。我们采用了该方法来估计野外大火对2005 - 2018年美国西部空气污染的因果影响。
Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial interference, taking into account that treatments at nearby locations are more influential than treatments at locations further apart. Under the no unmeasured confounding assumption, we show that a generalized propensity score is sufficient to remove all measured confounding. To reduce dimensionality issues, we propose a Bayesian spline-based regression model accounting for a sufficient set of variables for the generalized propensity score. A simulation study demonstrates the accuracy and coverage properties. We apply the method to estimate the causal effect of wildland fires on air pollution in the Western United States over 2005--2018.
