AAAI 2026technical0 citations
GAHMN: A Generative Approach for High-Dimensional Mediation Analysis
Jiaming Zhang, Yiqi Lin, Rou Zhang, Xinyuan Song, Hanwen Ning
Abstract
High-dimensional mediation analysis (HMA) seeks to uncover complex causal mechanisms involving numerous mediators and plays a crucial role in scientific and social sciences. In this work, we introduce the Generative Adversarial High-dimensional Mediation Network (GAHMN), a novel, scalable structured generative framework designed for causal analysis in high-dimensional settings. GAHMN formulates mediation analysis as dual conditional generative blocks, explicitly capturing mediators
BibTeX
@inproceedings{aaai2026_gahmnagenerative,
title = {GAHMN: A Generative Approach for High-Dimensional Mediation Analysis},
author = {Jiaming Zhang and Yiqi Lin and Rou Zhang and Xinyuan Song and Hanwen Ning},
booktitle = {AAAI 2026},
year = {2026}
}