arXiv Machine Learning June 5, 2026 By Hazhir Aliahmadi, Irina Babayan, Greg van Anders

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

Read the original on arXiv Machine Learning →

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.

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 Machine Learning.

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

multimodal 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

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

Aug 5

GENESIS: Towards Explainable Causal Discovery

arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.

By Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat, Harsh Vardhan Singh Chauhan, Niranjan Pedanekar

llms benchmarks

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 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

One email a morning, machine-written

Related stories

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

Interpretable Causal Discovery via Causal-Effect Constraints

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

GENESIS: Towards Explainable Causal Discovery

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

Amortized Bayesian Causal Discovery of Extended Factor Graphs