Research map / Probabilistic and scientific modeling / Causal discovery
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IGSP instead of perfect-IGSP for inhibiting interventions | Causal DAG recovery · Simulated datasets | Hamming distance | Helped |
| Use IGSP instead of perfect-IGSP for imperfect interventions | Causal DAG recovery · Simulated datasets | Hamming distance | Helped |
| Use IGSP instead of perfect-IGSP for perfect interventions | Causal DAG recovery · Simulated datasets | Hamming distance | No clear effect |
| Use IGSP instead of GIES | Causal DAG recovery · Simulated datasets | Hamming distance | Helped |
| Use GIES instead of IGSP when the number of interventional datasets is 10 | Causal DAG recovery · Simulated datasets | Hamming distance | Helped |
| Use IGSP or perfect-IGSP instead of GIES | Protein network recovery · Protein Expression Dataset | ROC performance | Helped |
| Use IGSP instead of perfect-IGSP | Protein network recovery · Protein Expression Dataset | ROC performance | No clear effect |
| Use IGSP or perfect-IGSP instead of GIES | Causal DAG prediction · Gene Expression Dataset | Intervention-effect prediction accuracy | Helped |
| Use IGSP instead of perfect-IGSP | Causal DAG prediction · Gene Expression Dataset | Intervention-effect prediction accuracy | No clear effect |
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- Learning Mixtures of Unknown Causal Interventions (NeurIPS 2024 · similarity 0.88)
- Coarsening Linear Non-Gaussian Causal Models with Cycles (NeurIPS 2026 · similarity 0.88)
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions (NeurIPS 2024 · similarity 0.88)
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