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From Local to Cluster: A Unified Framework for Causal Discovery with Latent Vari

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1 SIGNALFIRST DETECTED 28 April 2026
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28 Apr
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18 Sept
MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation

arXiv:2609.19944v1 Announce Type: new Abstract: Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission

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13 Jul
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding

arXiv:2607.09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs)

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27 Aug
Partial Identification under Causal Orders by Linear Programming

arXiv:2608.24427v1 Announce Type: new Abstract: Non-parametric (partial) identification of counterfactual queries typically relies on a fully specified causal graph. Motivated by

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19 May
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data

arXiv:2605.15353v1 Announce Type: cross Abstract: Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in

REPORT · WHERE · ArXiv CS.AIOPEN STORY
31 Aug
LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation

arXiv:2608.27472v1 Announce Type: new Abstract: Bayesian network structure learning (BNSL) from observational data struggles with orientation identifiability, while large language

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