论文标题
因果无污染的因果零的非参数测试
Nonparametric tests of the causal null with non-discrete exposures
论文作者
论文摘要
在许多科学研究中，确定暴露是否会对结果产生因果影响，这是令人感兴趣的。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淆变量以及关注的暴露是离散时，已经开发了许多方法来检验因果效应的存在。在本文中，我们提出了一类无原假设的非参数检验，即在观察到的混杂存在的情况下，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，连续和混合离散连续的暴露。我们证明我们提出的测试是双重的一致的，如果在该问题中涉及的两个滋扰参数都以足够快的速度估算，并且它们具有正确的渐近I型误差，并且它们有能力以$ n^{ - 1/2} $检测局部替代方案。我们研究了在数值研究中测试的表现，并使用它们来测试吸烟母亲中吸烟对出生体重的因果作用。
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 smoking on birthweight among smoking mothers.
