Cookies?
Library Header Image
LSE Research Online LSE Library Services

Testing mediation effects using logic of Boolean matrices

Shi, Chengchun and Li, Lexin (2021) Testing mediation effects using logic of Boolean matrices. Journal of the American Statistical Association. ISSN 0162-1459

[img] Text (Testing Mediation Effects Using Logic of Boolean Matrices) - Accepted Version
Repository staff only until 1 March 2022.

Download (2MB) | Request a copy

Identification Number: 10.1080/01621459.2021.1895177

Abstract

A central question in high-dimensional mediation analysis is to infer the significance of individual mediators. The main challenge is that the total number of potential paths that go through any mediator is super-exponential in the number of mediators. Most existing mediation inference solutions either explicitly impose that the mediators are conditionally independent given the exposure, or ignore any potential directed paths among the mediators. In this article, we propose a novel hypothesis testing procedure to evaluate individual mediation effects, while taking into account potential interactions among the mediators. Our proposal thus fills a crucial gap, and greatly extends the scope of existing mediation tests. Our key idea is to construct the test statistic using the logic of Boolean matrices, which enables us to establish the proper limiting distribution under the null hypothesis. We further employ screening, data splitting, and decorrelated estimation to reduce the bias and increase the power of the test. We show that our test can control both the size and false discovery rate asymptotically, and the power of the test approaches one, while allowing the number of mediators to diverge to infinity with the sample size. We demonstrate the efficacy of the method through simulations and a neuroimaging study of Alzheimer’s disease. A Python implementation of the proposed procedure is available at https://github. com/callmespring/LOGAN.

Item Type: Article
Official URL: https://www.tandfonline.com/toc/uasa20/current
Additional Information: © 2021 American Statistical Association
Divisions: Statistics
Subjects: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Date Deposited: 22 Feb 2021 11:21
Last Modified: 20 Sep 2021 04:12
URI: http://eprints.lse.ac.uk/id/eprint/108881

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics