Research map / Probabilistic and scientific modeling / Causal discovery
Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge
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 |
|---|---|---|---|
| select intervention variable by maximum entropy with MH sampling instead of randomly (ablation) | active causal discovery with latent variables · simulated DAGs d=10, p=0.10 | # interventions: 2.88 ± 1.28 -> 2.77 ± 1.19 | Helped |
| select intervention variable by maximum entropy instead of randomly (ablation) | active causal discovery with latent variables · simulated DAGs d=10, p=0.20 | # interventions: 3.59 ± 1.22 -> 3.42 ± 1.16 | Helped |
| select intervention variable by maximum entropy instead of randomly (ablation) | active causal discovery with latent variables · simulated DAGs d=10, p=0.25 | # interventions: 3.47 ± 1.34 -> 3.22 ± 1.19 | Helped |
| select intervention variable by maximum entropy instead of randomly (ablation) | active causal discovery with latent variables · simulated DAGs d=10 | Norm. SHD: 0.08 ± 0.14 -> 0.08 ± 0.14 | No clear effect |
Closest papers by meaning
- Integrating Background Knowledge for Scalable Causal Discovery (NeurIPS 2026 · similarity 0.90)
- Differentiable Causal Discovery for Latent Hierarchical Causal Models (ICLR 2025 · similarity 0.90)
- Local Identifying Causal Relations in the Presence of Latent Variables (ICML 2025 · similarity 0.90)
- Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants (AAAI 2024 · similarity 0.89)
- Bayesian Active Learning for Bivariate Causal Discovery (ICML 2025 · similarity 0.88)
- Identifiability Guarantees for Causal Disentanglement from Purely Observational Data (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.