A general model-based causal inference method overcomes the curse of synchrony and indirect effect
Abstract To identify causation, model-free inference methods, such as Granger Causality, have been widely used due to their flexibility. However, they have difficulty distinguishing synchrony and indirect effects from direct causation, leading to false predictions. To overcome this, model-based infe...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-39983-4 |