arXiv AI
Jun 9
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.
Overcoming Challenges of Interpretive Structural Modeling with Large Language Models
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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).
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.
Hugging Face Trending Papers
Sep 17
MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation
MaSCoD is a multi‑agent framework that explicitly addresses the premature omission of causal relations by first organizing candidate third variables and local structural patterns before making direct‑edge judgments. Using GPT‑5.4 and GPT‑4o as backbones, the study evaluates MaSCoD on Auto‑MPG, DWD, and Sachs datasets, finding that the Full phase—where structural hypotheses are supplied early—generally improves recall and F1 scores compared to a No Phase 1 approach, though it can also raise false‑positive rates. Ablation experiments reveal that providing both structural and contextual information does not always outperform providing only one, and that the benefits of the Full phase vary across datasets and backbones. whyItMatters":"The paper demonstrates that explicitly pre‑organizing structural context can improve causal graph generation performance, highlighting the importance of designing omission control mechanisms in causal discovery with large language models."
arXiv AI
Sep 18
MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation
MaSCoD is a multi‑agent framework that explicitly organizes candidate third variables and local structural patterns before making direct‑edge judgments in causal discovery. Using GPT‑5.4 and GPT‑4o on datasets such as Auto‑MPG, DWD, and Sachs, the study shows that providing structural hypotheses in a separate phase (Full) generally improves recall and F1 scores compared to constructing them during judgment, though it can also raise false‑positive rates. Ablation experiments reveal that the benefit of supplying both structural components is not guaranteed, and the advantage varies across datasets and model backbones.
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.
Hugging Face Trending Papers
Sep 10
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
CausalArena is a unified, evolvable benchmark designed to evaluate causal discovery methods across diverse structural causal models (SCMs). It incorporates synthetic SCMs for controlled structural variation, semantic operational SCMs for human-auditable environments, and formula-grounded SCMs to test discovery under explicit scientific mechanisms, along with real-world datasets for external validity. Experiments show that performance rankings vary significantly across SCM families and protocols, indicating that strong results on one benchmark do not generalize to others, especially in the context of causal discovery foundation models.
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.
arXiv Machine Learning
Sep 11
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
CausalArena is a new benchmark designed to evaluate causal discovery methods in the era of foundation models. It unifies synthetic structural causal models (SCMs), semantically grounded SCMs, and formula‑grounded SCMs, while also including real‑world datasets for external validation. Experiments show that performance rankings vary widely across different SCM families and protocols, indicating that strong results on one benchmark do not necessarily transfer to others.
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.
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.
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...