arXiv AI August 6, 2026 By Shrenik Zinage

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.

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

benchmarks

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 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

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 Machine Learning

Jul 14

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).

By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang

llms benchmarks

arXiv AI

2d ago

Structure-agnostic Causal Representation Learning

arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requi...

By Arman Behnam, Binghui Wang

benchmarks

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Related stories

Differentiable Causal Discovery for Singular Linear Models under Confounding

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

Interpretable Causal Discovery via Causal-Effect Constraints

Amortized Bayesian Causal Discovery of Extended Factor Graphs

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

Structure-agnostic Causal Representation Learning