Summary of Comprehensive Review and Empirical Evaluation Of Causal Discovery Algorithms For Numerical Data, by Wenjin Niu et al.
Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data
by Wenjin Niu, Zijun Gao, Liyan Song, Lingbo Li
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This study aims to provide a comprehensive understanding of causal discovery algorithms by conducting an exhaustive review and empirical evaluation on numerical data. The research begins with a literature review spanning over two decades, analyzing over 200 academic articles and identifying more than 40 representative algorithms. A structured taxonomy is developed, categorizing methods into six main types based on their complexities. An extensive empirical assessment of 29 causal discovery algorithms is conducted on multiple synthetic and real-world datasets using five evaluation metrics, providing top-3 algorithm recommendations and guidelines for users in various data scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal analysis is important because it helps us understand what causes things to happen. But the way scientists find causal relationships is messy and confusing. This study tries to fix that by organizing all the different methods scientists use into six main categories. It also tests 29 of these methods on lots of different data sets to see which ones work best in different situations. The results show that the type of data matters a lot, and it helps us understand how to choose the right method for our specific problem. |
