Summary of Weakly-supervised Causal Discovery Based on Fuzzy Knowledge and Complex Data Complementarity, by Wenrui Li et al.
Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity
by Wenrui Li, Wei Zhang, Qinghao Zhang, Xuegong Zhang, Xiaowo Wang
First submitted to arxiv on: 14 May 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 weakly-supervised fuzzy knowledge and data co-driven causal discovery method called KEEL, which adopts a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge. KEEL forms weakened constraints that lessen dependency on expertise and allow limited and error-prone fuzzy knowledge to guide causal discovery. It enhances generalization and robustness, especially in high-dimensional and small-sample scenarios. The method integrates the extended linear causal model (ELCM) for dealing with multi-distribution and incomplete data. Extensive experiments demonstrate KEEL’s superiority over state-of-the-art methods in accuracy, robustness, and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KEEL is a new way to figure out cause-and-effect relationships from data, which is important for understanding complex systems. The problem is that current methods don’t work well when we have limited information or high-dimensional data with small sample sizes. KEEL uses a special kind of knowledge called “fuzzy” to help find the right causes and effects. It’s better than other methods at finding the correct relationships, even when there’s not much data. This is important for things like understanding how proteins work in our bodies. |
Keywords
» Artificial intelligence » Generalization » Supervised
