Nonparametric Tests of the Causal Null With Nondiscrete Exposures

• 成果类型: Article
• 署名作者: Westling, Ted
• 署名单位: University of Massachusetts System; University of Massachusetts Amherst
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1865168
• 发表日期: 2022
• 页码: 1551-1562
• 关键词: inference

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. for this article are available online.