arXiv AI June 3, 2026 By Bo Peng, Kaiwen Wu, Sirui Chen, Zhiheng Wang, Yu Qiao, Chaochao Lu

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery

arXiv:2606. 03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes.

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.

llms

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

Aug 26

From Causal Plausibility to Causal Reliability: Evaluating LLMs as Calibrated Direct Causal-Edge Classifiers

The study evaluates 12 instruction‑tuned open‑weight LLMs on six causal‑graph benchmarks, testing five prompting strategies and four confidence sources. Findings show that LLMs tend to over‑predict edges, misclassify indirect or reversed edges as direct, and exhibit high over‑confidence, while conventional confidence estimates are unreliable and agreement signals offer limited improvement. The results suggest LLMs should be used as externally validated soft causal priors rather than definitive causal‑structure evidence.

By Amit Kumar, Elnur Adl Zarabi, Suranjana Trivedy, Zhiqian Chen, Lei Zhang, Kaiqun Fu, Taoran Ji

llms benchmarks

arXiv Machine Learning

Aug 5

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

arXiv:2608. 02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive models.

By Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes, Mahdi Bagheri, Pratik Gajane, Amir M. Rahmani

llms benchmarks safety

arXiv Machine Learning

4d ago

CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

arXiv:2609.36689v1 Announce Type: new Abstract: Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that v...

By Wenjin Liu, Chenxi Wang, Yue Lu, Zhe Cui, Haoran Luo

llms benchmarks safety

arXiv Machine Learning

Sep 10

CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

arXiv:2404.06349v3 Announce Type: replace Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual r...

By Yu Zhou, Xingyu Wu, Jibin Wu, Liang Feng, Kay Chen Tan

llms benchmarks

arXiv AI

Aug 6

Can Post-Training Transform LLMs into Causal Reasoners?

arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.

By Junqi Chen, Sirui Chen, Chaochao Lu

llms reinforcement-learning fine-tuning benchmarks

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Related stories

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

From Causal Plausibility to Causal Reliability: Evaluating LLMs as Calibrated Direct Causal-Edge Classifiers

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

Can Post-Training Transform LLMs into Causal Reasoners?