ArticleJournal of the American Statistical Association2022
Testing Mediation Effects Using Logic of Boolean Matrices.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Causal mediation analysis: selection with asymptotically valid inference.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD.bioRxiv : the preprint server for biology · 2025Article
- MULTIVARIATE DYNAMIC MEDIATION ANALYSIS UNDER A REINFORCEMENT LEARNING FRAMEWORK.Annals of statistics · 2025Article
- Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Discovery and inference of a causal network with hidden confounding.Journal of the American Statistical Association · 2024Article
- Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data.Computational statistics & data analysis · 2023Article
- Article
- On optimal two-stage testing of multiple mediators.Biometrical journal. Biometrische Zeitschrift · 2022Article
- HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data.BMC bioinformatics · 2022Article
- Mediation analysis for survival data with high-dimensional mediators.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.