Research map / Probabilistic and scientific modeling / Causal discovery
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a learned time-varying causal model for forecasting | Nonstationary synthetic time series · Synthetic data, changing causal strengths; changing causal strengths and noise variances | RMSE | Helped |
| Use a learned time-varying causal model for forecasting | Nonstationary synthetic time series · Synthetic data, changing causal strengths; changing causal strengths and noise variances | RMSE: 0.32 vs 0.38; 0.42; 0.43; 0.37; 0.37 | Helped |
| Model causal strengths and noise variances as time-varying | Proposed causal discovery method · Synthetic data with changing causal strengths and noise variances | F1 score | Helped |
| Use a learned time-varying causal model for forecasting | Inflation forecasting · USA inflation (2007–2017) | RMSE: 0.32 vs 0.38; 0.42; 0.43; 0.37; 0.37 | Helped |
| Increase the synthetic training sample size | Proposed method · Synthetic causal discovery and forecasting | F1 score | Helped |
| Increase the synthetic training sample size | Forecasting methods · Synthetic one-step-ahead forecasting | RMSE | Helped |
| Use LiNGAM on nonstationary rather than fixed causal data | LiNGAM · Synthetic causal discovery | F1 score | Hurt |
| Use Lasso or GP without modeling distribution change | Lasso and GP · Synthetic one-step-ahead forecasting | RMSE | Hurt |
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