Evidence map›Paper›PMID 39416711›Full record

ArticleJournal of the American Statistical Association2024

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning.

Chengchun Shi, Yunzhe Zhou, Lexin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Causal Discovery with Generalized Linear Models through Peeling Algorithms.Journal of machine learning research : JMLR · 2024
    Article
  2. Discovery and inference of a causal network with hidden confounding.Journal of the American Statistical Association · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chengchun ShiLondon School of Economics and Political Science.
Yunzhe ZhouUniversity of California at Berkeley.
Lexin LiUniversity of California at Berkeley.

Funding

Mechanisms of Alzheimer's Disease Progression in the Aging BrainR01AG062542 · NIA · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI JAGUST, WILLIAM J. · 2019 to 2023
$4.2M
New Statistical Methods for Multicenter Multimodal Longitudinal Neuroimaging AnalysisR01AG061303 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI LI, LEXIN · 2019 to 2022
$1.5M
NIA NIH HHS R01 AG061303NIA NIH HHS R01 AG062542
6 · The paper itself

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.

Indexed as

Brain connectivity networksDirected acyclic graphGenerative adversarial networksHypothesis testingMultilayer perceptron neural networks

Identifiers

PMID39416711
PMCPMC11474452

What OpenQuestion holds

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Registered trials

None linked

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.