Research map / Probabilistic and scientific modeling / Causal discovery
Recursively Learning Causal Structures Using Regression-Based Conditional Independence Test
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| CAPA causal partitioning with ReCIT orientation vs SADA-LiNGAM | CAPA · Pigs (441 nodes) | F1: 0.34 -> 0.81 | Helped |
| CAPA vs SADA-LiNGAM | CAPA · Pathfinder | F1: 0.16 -> 0.64 | Helped |
| CAPA vs SADA-LiNGAM | CAPA · 8 real-world networks | precision: 0.45 -> 0.78 (Insurance) | Helped |
| CAPA vs SADA-LiNGAM | CAPA · 8 real-world networks | recall: 0.81 -> 0.59 (Pathfinder) | Mixed |
| CAPA vs SADA-LiNGAM running time | CAPA · Pigs | elapsed time (s): 1 · 106 -> 1 · 104 | Helped |
| CAPA causal partitioning (d-separation preserving) vs SADA partitioning (ablation) | PC base algorithm · simulated skeleton learning, 100 nodes | precision and F1 | Mixed |
| orient edges with regression-based CI (ReCIT) in addition to V-structures (ablation) | CAPA vs SADA · simulated structure learning | recall, precision, F1 | Helped |
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