Research map / Probabilistic and scientific modeling / Causal discovery
Improving Causal Discovery By Optimal Bayesian Network Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use optimal exhaustive BIC-score search instead of GES | Causal discovery on the unfaithful DAG in Figure 1 · Unfaithful DAG, Figure 1 | BIC score: -10801 -> -10806 | Helped |
| Use exhaustive search rather than PC or GES | Causal discovery with faithfulness violations · Unfaithful DAGs in Figures 1 and 2 | MEC recovery | Helped |
| Use A* rather than GES | Causal discovery on finite samples from random linear Gaussian networks · 30 random networks, 20 variables each | F1 score | Helped |
| Use Triplet A* rather than GES | Causal discovery on larger networks · 60-variable networks | F1 score | Mixed |
| Use Triplet A* rather than GES | Causal discovery on larger networks · 60-variable networks | orientation recall | Hurt |
| Vary the BIC penalty coefficient λ | GES and A* · Large-sample experiments | learned structure | No clear effect |
Closest papers by meaning
- On Causal Discovery in the Presence of Deterministic Relations (NeurIPS 2024 · similarity 0.91)
- Less Greedy Equivalence Search (NeurIPS 2025 · similarity 0.89)
- Adaptive Online Experimental Design for Causal Discovery (ICML 2024 · similarity 0.88)
- Differentiable Constraint-Based Causal Discovery (NeurIPS 2025 · similarity 0.88)
- A Meta-Learning Approach to Bayesian Causal Discovery (ICLR 2025 · similarity 0.88)
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions (NeurIPS 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.