arXiv AI

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.

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

arXiv Machine Learning

Jul 14

Integrating Background Knowledge for Scalable Causal Discovery

arXiv:2607. 10456v1 Announce Type: cross Abstract: Expert background knowledge is often available in practical applications of causal discovery.

By M\'aty\'as Schubert, Theofanis Aslanidis, Tom Claassen, Sara Magliacane

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

Jul 28

Structure Over Scale: Schema-Constrained Causal Graphs for RAG

arXiv:2607. 22592v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) grounds answers in structured knowledge, but current systems extract entities and relationships exhaustively, producing graphs whose size and construction cost scale with corpus length rather than with the reasoning a query requires.

By Marc Saouda (Boston Consulting Group), Rajprakash Bale (Boston Consulting Group), Eren Aldis (Boston Consulting Group), Cloves Almeida (Boston Consulting Group)

llms rag benchmarks

arXiv AI

5d ago

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

Jun 5

LLM Explainability with Counterfactual Chains and Causal Graphs

arXiv:2606. 05972v1 Announce Type: new Abstract: Causal graphs provide a high-level language for making mechanisms transparent.

By Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor, Roi Reichart

llms nlp

arXiv AI

Jun 24

Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization

arXiv:2605. 01482v3 Announce Type: replace Abstract: Multi-Hop Fact Verification requires complex reasoning across disparate evidence, posing significant challenges for Large Language Models , which may suffer from hallucinations and fractured logical chains.

By Yunhan Bu, Quan Zhang, Huaping Zhang, Guotong Geng, Chunxiao Gao, Askar Hamdulla, Juan Wang, Qiuchi Li, Baohua Zhang, Shuai Lei, Yunbo Cao, Zhunchen Luo

llms reinforcement-learning safety

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