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.
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.
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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 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.
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.
arXiv AI
Jun 3
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.
arXiv Machine Learning
Aug 11
ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery
arXiv:2605. 16902v2 Announce Type: replace Abstract: Scientific artifacts such as models and datasets are foundations for research.
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.
arXiv AI
Jul 29
EviDAG: Auditable Causal DAG Authoring with Biomedical Literature
arXiv:2607. 21859v2 Announce Type: replace Abstract: Constructing causal directed acyclic graphs (DAGs) is a core step in biomedical causal analysis, yet it remains a largely manual process.
arXiv Machine Learning
Jun 25
CauScale: Neural Causal Discovery at Scale
arXiv:2602. 08629v2 Announce Type: replace Abstract: Causal discovery is essential for advancing data-driven fields such as scientific AI and data analysis, yet existing approaches face significant time- and space-efficiency bottlenecks when scaling to large graphs.
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.
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.
arXiv Machine Learning
Jun 19
Unsupervised Causal Abstractions Discovery
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.