Summary of Deciphering Interventional Dynamical Causality From Non-intervention Systems, by Jifan Shi et al.
Deciphering interventional dynamical causality from non-intervention systems
by Jifan Shi, Yang Li, Juan Zhao, Siyang Leng, Kazuyuki Aihara, Luonan Chen, Wei Lin
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM); Methodology (stat.ME); Machine Learning (stat.ML)
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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 proposed Interventional Dynamical Causality (IntDC) framework and its computational criterion, Interventional Embedding Entropy (IEE), enable the detection and quantification of causality in non-intervention systems. This is achieved by deciphering IntDC solely from observational time-series data, without requiring knowledge of dynamical models or real interventions. The IEE criterion demonstrated accuracy and robustness on benchmark simulated systems as well as real-world applications, including neural connectomes, COVID-19 transmission networks, and regulatory networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Detecting causality is important in many fields. This paper proposes a new way to do this without needing special knowledge or data from experiments. It’s called Interventional Dynamical Causality (IntDC). The authors also created a tool, called Interventional Embedding Entropy (IEE), that can be used to understand IntDC just by looking at data over time. This method was tested on some examples and worked well. |
Keywords
* Artificial intelligence * Embedding * Time series
