arXiv AI June 9, 2026 By Liesbeth Allein, Marie-Francine Moens

Implicit Causal Graph Construction in Text via Chain Discovery

arXiv:2606. 07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv AI

Jun 2

Consistency evaluation of benchmarks used for causal discovery

arXiv:2606. 01789v1 Announce Type: new Abstract: In graphical causal model, causal discovery aims to construct a causal graph based on numerical data and domain knowledge in plain text.

By Yuzhe Zhang, Chihui Chen, Lina Yao, Chen Wang

llms benchmarks

arXiv AI

Aug 12

Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach

arXiv:2602. 16481v2 Announce Type: replace Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions.

By Zihao Li, Fabrizio Russo

llms benchmarks safety

arXiv AI

Jun 10

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.

By Xinyu Li, Yuanyuan Wang, Haoxuan Li, Chuan Zhou, Erdun Gao, Bo Han, Tongliang Liu, Kun Zhang, Howard Bondell, Mingming Gong

llms agents safety

arXiv AI

Jul 20

Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction

arXiv:2607. 15281v1 Announce Type: new Abstract: Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms.

By Su Lan, Xuefei Yin, Yanming Zhu, Alan Wee-Chung Liew

llms nlp benchmarks

arXiv Machine Learning

Jun 26

Use What You Know: Causal Foundation Models with Partial Graphs

arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.

By Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf

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

Related stories

Consistency evaluation of benchmarks used for causal discovery

Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction

Use What You Know: Causal Foundation Models with Partial Graphs

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs