Research map / Probabilistic and scientific modeling / Causal discovery
Scalable Computation of Causal Bounds
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 |
|---|---|---|---|
| Aggregate response-function variables into valid hyperarc variables and construct the pruned LP directly | Causal-bound LP construction without additional observations · Ex A, Ex B, Ex C, Ex F, and Ex G | runtime (s): Ex A: 2.0 vs >1200; Ex B: 3.9 vs 72.6; Ex C: 3.7 vs >1200; Ex F: 0.6 vs >1200; Ex G: 2.4 vs >1200 | Helped |
| Aggregate response-function variables into valid hyperarc variables and construct the pruned LP directly | Causal-bound LP construction with additional observations · Ex A, Ex B, Ex C, Ex F, and Ex G | runtime (s): Ex A: 2.1 vs >1200; Ex B: 4.5 vs 74.4; Ex C: 4.3 vs >1200; Ex F: 0.7 vs >1200; Ex G: 3.1 vs >1200 | Helped |
Closest papers by meaning
- Optimal Experiments for Partial Causal Effect Identification (NeurIPS 2026 · similarity 0.88)
- Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis (ICML 2024 · similarity 0.87)
- Foundations of Testing for Finite-Sample Causal Discovery (ICML 2024 · similarity 0.87)
- Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding (NeurIPS 2024 · similarity 0.86)
- Scores for Learning Discrete Causal Graphs with Unobserved Confounders (AAAI 2024 · similarity 0.86)
- Linear Causal Bandits: Unknown Graph and Soft Interventions (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.