Research map / Graph learning / Graph algorithms and clustering
Experimental Design for Learning Causal Graphs with Latent Variables
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Randomized observable-graph learning (Algorithm 3) using transitive reductions over random interventions | observable causal graph learning · theoretical analysis | number of interventions | Helped |
| Deterministic observable-graph learning by intervening on layers of the transitive closure (Algorithm 2) | observable causal graph learning · theoretical analysis | number of interventions | Helped |
| Learning latents between non-adjacent nodes by intervening on union of parent sets of independent-set covers (Algorithm 4) instead of per-pair interventions | latent variable detection between non-edges · theoretical analysis | number of interventions | Helped |
| Do-see test with strong edge coloring to detect latents between adjacent nodes (Algorithm 5) | latent detection between adjacent nodes · theoretical analysis | number of interventions | Helped |
Closest papers by meaning
- Characterization and Learning of Causal Graphs from Hard Interventions (NeurIPS 2025 · similarity 0.91)
- Local Identifying Causal Relations in the Presence of Latent Variables (ICML 2025 · similarity 0.91)
- Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits (NeurIPS 2024 · similarity 0.89)
- Differentiable Causal Discovery for Latent Hierarchical Causal Models (ICLR 2025 · similarity 0.89)
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions (NeurIPS 2024 · similarity 0.89)
- Adaptive Online Experimental Design for Causal Discovery (ICML 2024 · similarity 0.89)
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