Abstract

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.

摘要

在许多科学研究中，确定暴露是否对结果有因果关系是很有意义的。在观察性研究中，由于存在影响暴露和结果的混杂变量，这是一项艰巨的任务。当观察到所有这些混杂变量并且关注的暴露是离散的时，已经开发出许多方法来测试是否存在因果效应。在本文中，我们提出了一类非参数假设的非参数检验，即在存在观察到的混淆的情况下，任意单变量暴露对结果没有平均因果关系。我们的测试适用于离散，连续和混合的离散连续曝光。我们证明了我们提出的测试具有双重鲁棒性，${ñ}^{-\mathrm{1个}/2}$。我们在数值研究中研究测试的性能，并在早期疫苗试验中使用它们来测试BMI对免疫应答的因果关系。