SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery
Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data. The resulting posterior allows systematic reasoning regarding epistemic uncertainty within these theories. Nonetheless, finding such graphs is difficult due to identifiability problems and limited observational data. Furthermore, precisely approximating posterior over graphs is challenging given vast range of potential DAGs. Recent Bayesian approaches have addressed some of these challenges, yet they remain limited as they fail to encode dependencies between edges, and lack principled ways to incorporate domain ...
| Authors | Shrenik Zinage |
|---|---|
| Primary category | cs.LG |
| Published | 5 August 2026 |
| Last updated at source | 23 August 2026 |
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