Testing directed acyclic graph via structural, supervised and generative adversarial learning

Chengchun Shi, Yunzhe Zhou and Lexin Li

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods often impose some specific model structures such as linear models or additive models, and assume independent data observations. Our proposed test instead allows the associations among the random variables to be nonlinear and the data to be time-dependent. We build the test based on some highly flexible neural networks learners. We establish the asymptotic guarantees of the test, while allowing either the number of subjects or the number of time points for each subject to diverge to infinity. We demonstrate the efficacy of the test through simulations and a brain connectivity network analysis. Supplementary materials for this article are available online.

Keywords: brain connectivity networks; directed acrylic graph; hypothesis testing; generative adversarial networks; multilayer perceptron neural networks; Hypothesis testing; CIF-2102227; R01AG061303; R01AG062542; EP/W014971/1 (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2024-12-31
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-mac and nep-net
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Published in Journal of the American Statistical Association, 31, December, 2024, 119(547), pp. 1833 - 1846. ISSN: 0162-1459

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:119446

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