← Back to feed
11

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

The study analyzes how latent confounding affects the posterior distribution over directed acyclic graphs in Bayesian causal discovery.

Impact
38/100
Current rank score
11.36
Source tier
Tier 1
Category
Research
Read the full story at arxiv.org

Firefly links to the original publisher. The summary above is AI-generated for orientation and may differ from the source. The “current rank score” decays over time so newer significant stories surface first.