mlsanityData to 7 Oct 2026

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

arXiv 2106.01474 ↗First posted 2 Jun 2021stat.ML cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 78 references that are in the map. First posted on arXiv 2 Jun 2021.

Builds on

19 of 78 references.

References · 19

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7 more
  1. 2019Causal Discovery with Reinforcement Learningcited by 145
  2. 2018Deep Neural Networks Learn Non-Smooth Functions Effectivelycited by 57
  3. 2020Double Generative Adversarial Networks for Conditional Independence Testingcited by 21
  4. 2014Estimating the effect of joint interventions from observational data in sparse high-dimensional settingscited by 20
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  6. 2018Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Modelscited by 7
  7. 2018Inference in high-dimensional graphical modelscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2025Doubly Robust Alignment for Large Language Modelscited by 11
  2. 2025Statistical Inference in Reinforcement Learning: A Selective Surveycited by 2
  3. 2023Causal Discovery with Generalized Linear Models through Peeling Algorithmscited by 1
  4. 2026Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inferencecited by 0