Research map / Probabilistic and scientific modeling / Causal discovery
Causal Inference via Kernel Deviance Measures
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use KCDC direct comparison of conditional-embedding deviance | KCDC · Additive noise synthetic datasets | classification accuracy: 100% | Helped |
| Use KCDC direct comparison of conditional-embedding deviance | KCDC · Multiplicative noise synthetic datasets | classification accuracy: 100% | Helped |
| Use KCDC direct comparison of conditional-embedding deviance | KCDC · Complex noise synthetic datasets | classification accuracy: 98%; 92%; 100% | Mixed |
| Use majority voting across kernel hyperparameters instead of direct comparison with one configuration | KCDC · Tübingen Cause-Effect Pairs | classification accuracy: 72.87% -> 78.71% | Helped |
| Use KCDC majority voting rather than the strongest listed baseline, CGNN | Tübingen Cause-Effect Pairs · Tübingen Cause-Effect Pairs | classification accuracy: 74.4% -> 78.71% | Helped |
| Use KCDC across tested kernel hyperparameters | UK, Canada, and USA GDP growth time series · Quarterly GDP growth, 1980–2011 | causal direction identification | Helped |
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