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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确提出在已观测混杂下检验平均因果效应的非参数方法。

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关注这篇论文的作者
Ted Westling

非离散暴露下因果零假设的非参数检验

研究问题
在存在已观测混杂因素的观察性研究中,如何检验任意单变量暴露对结局没有平均因果效应,并统一处理离散、连续及混合暴露?
方法与思路
作者提出一类非参数因果零假设检验,研究双重稳健一致性、干扰参数估计速度与渐近尺寸,并以数值研究和疫苗试验数据展示应用。
材料陈述的贡献
摘要称,检验具有双重稳健一致性;两类干扰参数估计足够快时具有正确渐近第一类错误率,并能检测以平方根样本量速度逼近的局部备择。
阅读关注点
应核对平均因果零假设的精确定义、混杂调整的识别条件、双重稳健性质与尺寸有效性的条件差别,以及局部备择的范围。
阅读边界
OpenAlex 索引稿件版本未注明,不能推断刊本变化。摘要未列出完整识别条件或干扰参数速度要求,不能据此推断对未观测混杂稳健。

原文摘要

In many scientific studies, it is of interest to determine whether an exposure has a causal effect on an outcome. In observational studies, this is a challenging task due to the presence of confounding variables that affect both the exposure and the outcome. Many methods have been developed to test for the presence of a causal effect when all such confounding variables are observed and when the exposure of interest is discrete. In this article, we propose a class of nonparametric tests of the null hypothesis that there is no average causal effect of an arbitrary univariate exposure on an outcome in the presence of observed confounding. Our tests apply to discrete, continuous, and mixed discrete-continuous exposures. We demonstrate that our proposed tests are doubly robust consistent, that they have correct asymptotic Type I error if both nuisance parameters involved in the problem are estimated at fast enough rates, and that they have power to detect local alternatives approaching the null at the rate n−1/2. We study the performance of our tests in numerical studies, and use them to test for the presence of a causal effect of BMI on immune response in early phase vaccine trials. Supplementary materials for this article are available online.

笔记与分组