Research map / Graph learning / Graph algorithms and clustering
Constraint-based Causal Structure Learning with Consistent Separating Sets
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 |
|---|---|---|---|
| enforce sepset consistency (iterative consistent PC-stable) instead of original PC-stable | PC-stable · BNlearn Hepar2/Insurance/Barley, N=1000 | recall at max F-score: Rec<0.15-0.35 -> Rec0.5 | Helped |
| enforce sepset consistency instead of original PC-stable | PC-stable · BNlearn Hepar2/Insurance/Barley, N=1000 | precision at max F-score: Prec>0.65 -> Prec0.5-0.6 | Hurt |
| enforce sepset consistency | PC-stable · scale-free DAGs, 50 nodes, linear SEM, N=500 | adjacency recall and precision | Helped |
| enforce sepset consistency | PC-stable · scale-free DAGs | fraction of valid d-separation sepsets | Helped |
| skeleton-level consistency (Alg. 4) instead of orientation-level consistency (Alg. 3) (ablation) | Consistent PC-stable · scale-free DAGs | valid d-separation and performance | Helped |
| larger sample size or stronger interactions | original PC-stable · scale-free DAGs | fraction of inconsistent sepsets | Helped |
Closest papers by meaning
- DCILP: A Distributed Approach for Large-Scale Causal Structure Learning (AAAI 2025 · similarity 0.89)
- Differentiable Structure Learning with Ancestral Constraints (ICML 2025 · similarity 0.88)
- Scores for Learning Discrete Causal Graphs with Unobserved Confounders (AAAI 2024 · similarity 0.88)
- Causal Discovery with Fewer Conditional Independence Tests (ICML 2024 · similarity 0.88)
- Differentiable Constraint-Based Causal Discovery (NeurIPS 2025 · similarity 0.87)
- Differentiable Structure Learning with Partial Orders (NeurIPS 2024 · similarity 0.86)
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.