Nonparametric Tests of the Causal Null With Nondiscrete Exposures

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. Supplementary materials for this article are available online.

KEYWORDS:

• Continuous exposure
• Dose-response
• G-computation
• Observational study
• Uniform inference

Supplementary Materials

The supplementary material contains proofs of all theorems, additional results from the numerical studies, and additional results from the application.

Acknowledgments

We are grateful for helpful feedback on this project from Dylan Small, Marco Carone, Alex Luedtke, the University of Pennsylvania Causal Inference Working Group, and three anonymous referees.