Research map / Probabilistic and scientific modeling / Causal discovery
Meek Separators and Their Applications in Targeted Causal Discovery
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 |
|---|---|---|---|
| MeekSep and MeekSep-1 versus Random, Coloring-FG and Coloring-NI baselines | Meek separator subset search · Subset search, r-hop model r=3, synthetic DAGs | number of interventions | Helped |
| MeekSep versus Random and Coloring-FG baselines | Meek separator causal mean matching · Causal mean matching, Erdos-Renyi 50 vertices | extra interventions over Verification | Helped |
| MeekSep versus CliqueTree strategy | Meek separator causal mean matching · Causal mean matching, Erdos-Renyi 50 vertices | extra interventions over Verification | No clear effect |
| MeekSep-1 variant that checks for termination after every intervention (ablation) | MeekSep · Subset search and causal mean matching | number of interventions | No clear effect |
Closest papers by meaning
- Causal Discovery with Fewer Conditional Independence Tests (ICML 2024 · similarity 0.89)
- DCILP: A Distributed Approach for Large-Scale Causal Structure Learning (AAAI 2025 · similarity 0.89)
- Interventional Causal Discovery in a Mixture of DAGs (NeurIPS 2024 · similarity 0.88)
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions (NeurIPS 2024 · similarity 0.88)
- Adaptive Online Experimental Design for Causal Discovery (ICML 2024 · similarity 0.88)
- Integrating Background Knowledge for Scalable Causal Discovery (NeurIPS 2026 · 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.
