Summary of Identifying Nonstationary Causal Structures with High-order Markov Switching Models, by Carles Balsells-rodas et al.
Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
by Carles Balsells-Rodas, Yixin Wang, Pedro A. M. Mediano, Yingzhen Li
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 causal discovery in time series data, addressing the limitation of traditional methods that assume stationarity. The authors introduce regime-dependent causal structures to handle nonstationary time series with time-dependent effects or heterogeneous noise. They establish identifiability for high-order Markov Switching Models and demonstrate the scalability of their method on brain activity data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to discover causes in time series data, which is important for understanding complex systems like climate patterns and brain activity. The researchers use a new approach that can handle changes over time and noise that varies across different parts of the data. They show that this approach works well on real-world data from brain activity. |
Keywords
* Artificial intelligence * Time series
