Summary of Jacobian Regularizer-based Neural Granger Causality, by Wanqi Zhou and Shuanghao Bai and Shujian Yu and Qibin Zhao and Badong Chen
Jacobian Regularizer-based Neural Granger Causality
by Wanqi Zhou, Shuanghao Bai, Shujian Yu, Qibin Zhao, Badong Chen
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to neural Granger causality, called JRNGC, which addresses several limitations of existing methods. The existing framework requires separate predictive models for each target variable, resulting in challenges in modeling complex relationships between variables and estimating Granger causality accurately. JRNGC uses a Jacobian Regularizer-based method to learn multivariate summary Granger causality and full-time Granger causality by constructing a single model for all target variables. The approach eliminates sparsity constraints on weights using an input-output Jacobian matrix regularizer, which can be represented as a weighted causal matrix in post-hoc analysis. The proposed method achieves competitive performance with state-of-the-art methods while maintaining lower model complexity and high scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve some problems with neural Granger causality. It’s hard to make models that work well for lots of variables at once, and it’s also tricky to figure out which variables are causing changes in others. The new method, called JRNGC, makes this easier by using a special kind of regularizer to help the model learn about all the relationships between variables at once. This helps the model be simpler and faster, while still being able to do a good job. |
