Research map / Probabilistic and scientific modeling / Causal discovery
Multi-domain Causal Structure Learning in Linear Systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IB method instead of MC method | two-variable causal direction · Synthetic model 1 | F1 score | Helped |
| Use MC method when parameters change little (model 2) | two-variable causal direction · Synthetic model 2 | F1 score | Helped |
| Use IB method instead of HSIC test with few domains | two-variable causal direction · Synthetic, limited domains | F1 score | Helped |
| Add a latent confounder to the system | IB method · Synthetic two-variable | F1 score | Hurt |
| Use IB and MC instead of HSIC for network order | multi-variable causal structure · Synthetic networks, p up to 12 | F1 score | Helped |
| Use IB/MC multi-domain methods instead of LiNGAM on pooled data | multi-variable causal structure · Synthetic networks | F1 score | Helped |
| Use denser (complete) graphs instead of sparse | IB and MC · Synthetic networks | F1 score | Helped |
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