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.
Causal Preference Elicitation
arXiv:2602. 01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs).
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SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery
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Use What You Know: Causal Foundation Models with Partial Graphs
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How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
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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.
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Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
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Lifted Causal Inference
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