Research map / Probabilistic and scientific modeling / Causal discovery
Causal Discovery in Hawkes Processes by Minimum Description Length
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MDL-based model selection with Monte Carlo model-complexity estimation instead of regularized maximum likelihood | Hawkes-process causal discovery · Synthetic (p=7, T=200) | F1: 68.7 -> 77.4 | Helped |
| Use MDL-based model selection with Monte Carlo model-complexity estimation instead of regularized maximum likelihood | Hawkes-process causal discovery · Synthetic (p=7, T=400) | F1: 74.6 -> 84.7 | Helped |
| Use MDL-based model selection with Monte Carlo model-complexity estimation instead of regularized maximum likelihood | Hawkes-process causal discovery · Synthetic (p=7, T=700) | F1: 80.4 -> 89.3 | Helped |
| Use MDL-based model selection with Monte Carlo model-complexity estimation instead of NPHC | Hawkes-process causal discovery · Synthetic (p=20, T=500) | F1: 27.3 -> 79.4 | Helped |
| Use MDL-based model selection with Monte Carlo model-complexity estimation instead of NPHC | Hawkes-process causal discovery · Synthetic (p=20, T=1300) | F1: 34.5 -> 82.8 | Helped |
| Use MDL-based model selection with Monte Carlo model-complexity estimation instead of NPHC | Hawkes-process causal discovery · Synthetic (p=20, T=2000) | F1: 40.0 -> 84.4 | Helped |
| Use MDLH rather than regularized ML | G-7 bonds causal discovery · G-7 bonds | graph plausibility against expert knowledge | Helped |
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