Research map / Probabilistic and scientific modeling / Causal discovery
Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| remove geographical distance graph (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7101 | Hurt |
| remove transition probability graph (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7124 | Hurt |
| remove dynamic causal graph (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7411 | Hurt |
| remove squeeze-excitation from dynamic causality generator (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7074 | Hurt |
| remove previous spatio-temporal state from dynamic causality generator (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7300 | Hurt |
| remove input-gate features from dynamic causality generator (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7105 | Hurt |
| replace input gate with fully connected feature extraction (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7266 | Hurt |
| remove input gate (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7239 | Hurt |
| remove counterfactual representation reasoning module (ablation) | STNSCM · NYC-Bike | MAE: 2.6701 -> 2.7041 | Hurt |
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