A new research paper analyzes the failure modes of Bayesian causal discovery in linear Gaussian causal models when latent confounding is present. The study identifies a critical correlation threshold beyond which the model favors graphs with spurious edges between confounded variables. This threshold decreases with increased sample size, meaning more data can paradoxically lead to incorrect conclusions under confounding. The research further characterizes two distinct posterior failure regimes based on the local structure around the confounded variables, supported by exact posterior computations. AI
IMPACT Highlights potential failure modes in causal discovery algorithms, crucial for reliable AI decision-making in complex environments.
RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical findings and analysis of a machine learning method.
- arXiv
- Bayesian causal discovery
- Dags
- linear Gaussian causal models
- correlation threshold
- DagsHub
- Hugging Face
- latent confounding
- sample size
- spurious edge
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