Research map / Probabilistic and scientific modeling / Causal discovery
Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ORION instead of LINGAM | Causal discovery across multiple environments · Synthetic data, m=10, d=3 | normalized SID: 0.58 -> 0.45 | Helped |
| Use ORION instead of LINGAM | Causal discovery across multiple environments · Synthetic data, m=10, d=5 | normalized SID: 0.55 -> 0.44 | Helped |
| Use ORION instead of LINGAM | Causal discovery across multiple environments · Synthetic data, m=10, d=7 | normalized SID: 0.53 -> 0.42 | Helped |
| Use ORION instead of LINGAM | Causal discovery across multiple environments · Synthetic data, m=10, d=9 | normalized SID: 0.52 -> 0.43 | Helped |
| Use ORION for intervention-target identification | Synthetic causal networks · Synthetic intervention-target experiment | F1: 0.63 | Helped |
| Use ORION rather than the compared methods | Causal discovery across multiple environments · Synthetic data with different intervention distributions | normalized SHD and SID | Helped |
| Use ORION rather than the compared methods | Causal discovery across multiple environments · REGED5, REGED15, REGED5s, and REGED15s | normalized SHD and SID | Helped |
Closest papers by meaning
- Adaptive Online Experimental Design for Causal Discovery (ICML 2024 · similarity 0.90)
- Interventional Causal Discovery in a Mixture of DAGs (NeurIPS 2024 · similarity 0.89)
- CausalTab: Pretraining Across Causal Environments for Tabular Causal Discovery (NeurIPS 2026 · similarity 0.89)
- Characterization and Learning of Causal Graphs from Hard Interventions (NeurIPS 2025 · similarity 0.89)
- Integrating Background Knowledge for Scalable Causal Discovery (NeurIPS 2026 · similarity 0.89)
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions (NeurIPS 2024 · similarity 0.89)
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