Research map / Probabilistic and scientific modeling / Causal discovery
Reinforcement Causal Structure Learning on Order Graph
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the learned order-graph transition model instead of CORL search | Causal discovery on 40-node DAGs · DAGs with 40 nodes | #search: 7.4×10^4 -> 3.6×10^4 | Helped |
| Use RCL-OG instead of CORL | Causal discovery on 40-node DAGs · DAGs with 40 nodes | TPR: 0.971 -> 0.978 | Helped |
| Use RCL-OG instead of B-PC | Probability sampling · Flow cytometry | E-correct: 4.95 -> 6.60 | Helped |
| Use RCL-OG instead of B-PC | Probability sampling · Flow cytometry | E-SHD: 13.10 -> 11.70 | Helped |
| Use RCL-OG instead of oMCMC | Probability sampling · Flow cytometry | E-SID: 55.75 -> 41.40 | Helped |
| Use RCL-OG instead of NOTEARS | Single-graph causal structure learning · Flow cytometry | SID: 44 -> 39 | Helped |
| Use RCL-OG instead of NOTEARS | Single-graph causal structure learning · Flow cytometry | correct: 7 -> 7 | No clear effect |
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