Summary of Causal Discovery From Time-series Data with Short-term Invariance-based Convolutional Neural Networks, by Rujia Shen et al.
Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks
by Rujia Shen, Boran Wang, Chao Zhao, Yi Guan, Jingchi Jiang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel gradient-based causal discovery approach STIC aims to uncover causality from time-series data, focusing on short-term and mechanism invariance using convolutional neural networks. The approach leverages both the short-term time and mechanism invariance of causality within each window observation, enhancing sample efficiency. To demonstrate the necessity of convolutional neural networks, the authors theoretically derive the equivalence between convolution and the underlying generative principle of time-series data. Experimental evaluations on synthetic and FMRI benchmark datasets show that STIC outperforms baselines significantly, achieving state-of-the-art performance when dealing with limited observed time steps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary STIC is a new way to find causes in time-series data. It’s like trying to figure out what’s causing changes over time. The approach uses special types of neural networks called convolutional neural networks to make this happen. By using these networks, STIC can be more efficient and get better results than other methods when there isn’t much data. This is important because it helps us understand how things change over time. |
Keywords
» Artificial intelligence » Time series
