How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
The paper investigates how Bayesian causal discovery behaves when latent confounding is present in linear Gaussian networks. It identifies a correlation threshold that lowers with larger sample sizes, causing the posterior to favor spurious edges between confounded variables. The authors also describe two distinct failure regimes of the posterior and validate their analysis with exact computations on various graph structures.
- ▪Bayesian causal discovery quantifies epistemic uncertainty over directed acyclic graphs through posterior inference.
- ▪The study focuses on linear Gaussian causal models with additive latent confounding between exactly two observed variables.
- ▪A critical correlation threshold is derived, which decreases as sample size increases, leading to a preference for spurious edges beyond the threshold.
- ▪Two distinct posterior failure regimes are characterized based on the local structure around the confounded variables.
- ▪The theoretical findings are supported by exact posterior calculations on multiple graph structures.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.09449 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
| Last seen | 2026-07-13T06:15:33.224Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | s5NqeUFHQy68 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2607.09449 (cs) [Submitted on 10 Jul 2026] Title:How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding Authors:Debargha Ghosh, Silja Renooij, Anna Kononova View a PDF of the paper titled How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding, by Debargha Ghosh and 2 other authors View PDF HTML (experimental) Abstract:Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
Discussion
0 commentsMore from arXiv.org
