Researchers are exploring new methods to evaluate and improve causal discovery using large language models (LLMs) and statistical techniques. One study found that LLMs often predict overly dense causal graphs with significant overconfidence, suggesting they are better suited as sources of soft causal priors rather than direct evidence of causal structure. Another paper introduces SURE-Ridge, a non-iterative estimator for linear Gaussian SEMs that performs well in sample-limited and compute-limited regimes. Additionally, a new approach uses variational inference to jointly discover latent clusters and causal structures, while another proposes a cross-validation method called Leave-One-Variable-Out (LOVO) to falsify causal discovery algorithms without ground truth. AI
IMPACT New methods for evaluating LLM causal reasoning and improving causal discovery could enhance AI's reliability in scientific applications.
RANK_REASON The cluster contains multiple academic papers detailing new research methodologies in causal discovery and LLM evaluation.
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- Dagmar
- GBNSL
- NOTEARS-MLP Algorithm
- Stein's unbiased risk estimate
- SURE-Ridge
- alphaXiv
- arXiv
- CatalyzeX
- causal discovery
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Leave-One-Variable-Out (LOVO)
- Litmaps
- LLMs
- ScienceCast
- scite Smart Citations
- variational inference
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