arXiv AI July 13, 2026 By Debargha Ghosh, Silja Renooij, Anna Kononova

How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding

arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 10

Differentiable Causal Discovery for Singular Linear Models under Confounding

arXiv:2601.01368v2 Announce Type: replace Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gr...

By Mujin Zhou, Ignavier Ng, Junzhe Zhang

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arXiv Machine Learning

Sep 17

Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders

arXiv:2609. 18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data.

By Weijian Yu, Jean Honorio

arXiv AI

Aug 6

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery

arXiv:2608. 04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.

By Shrenik Zinage

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arXiv Machine Learning

Jun 5

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.

By Hazhir Aliahmadi, Irina Babayan, Greg van Anders

arXiv Machine Learning

Jul 28

Amortized Bayesian Causal Discovery of Extended Factor Graphs

arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.

By Yichen Gu, Yuxuan Song, Weizhou Qian, Yixin Wang, Joshua Welch

benchmarks

arXiv AI

Aug 14

Interpretable Causal Discovery via Causal-Effect Constraints

arXiv:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.

By Cixuan Zhang, Guy Van den Broeck, Benjie Wang

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