论文标题
在观察研究中与因果关系的功能数据分析：功能处理的协变量平衡功能倾向评分
Functional Data Analysis with Causation in Observational Studies: Covariate Balancing Functional Propensity Score for Functional Treatments
论文作者
论文摘要
功能数据分析处理由曲线，表面，体积，歧管及以后引起的数据，在近几十年来是现代统计学和数据科学的一个快速发展的领域。功能变量对结果的影响是功能数据分析中的重要主题，但是大多数相关研究仅限于相关效应而不是因果效应。本文首次尝试研究功能变量作为观察性研究的治疗的因果效应。尽管功能处理缺乏概率密度函数，但倾向得分还是根据多元替代品的正确定义。提出了两种协变量方法来估计倾向评分，从而最大程度地减少了处理与协变量之间的相关性。一项模拟研究证明了该方法在协变量平衡和因果效应估计中的吸引力性能。提出的方法用于研究人体形状对人内脏脂肪组织的因果作用。
Functional data analysis, which handles data arising from curves, surfaces, volumes, manifolds and beyond in a variety of scientific fields, is a rapidly developing area in modern statistics and data science in the recent decades. The effect of a functional variable on an outcome is an essential theme in functional data analysis, but a majority of related studies are restricted to correlational effects rather than causal effects. This paper makes the first attempt to study the causal effect of a functional variable as a treatment in observational studies. Despite the lack of a probability density function for the functional treatment, the propensity score is properly defined in terms of a multivariate substitute. Two covariate balancing methods are proposed to estimate the propensity score, which minimize the correlation between the treatment and covariates. The appealing performance of the proposed method in both covariate balance and causal effect estimation is demonstrated by a simulation study. The proposed method is applied to study the causal effect of body shape on human visceral adipose tissue.
