Summary of Local Causal Discovery with Background Knowledge, by Qingyuan Zheng et al.
Local Causal Discovery with Background Knowledge
by Qingyuan Zheng, Yue Liu, Yangbo He
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 method leverages prior knowledge in causal graphical models to identify causal relationships. Building upon the framework of causal graphical models, previous works have shown that local structures can be learned solely by identifying causes or non-causes within Markov equivalent graphs. However, many applications involve partial knowledge of causal graphs, which can be used to further identify causal relationships. This paper introduces a method for learning local structures using various types of prior knowledge, including direct causal information and ancestral information. The authors also propose criteria for identifying causal relationships based on the learned local structure in the presence of prior knowledge. The method is applied to fair machine learning, demonstrating effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causality is important in many fields. This paper helps us figure out if one thing causes another or not. Usually, we only look at a small part of a bigger graph, but sometimes we have extra information that can help us understand more. The authors came up with a new way to use this prior knowledge to learn more about the relationships between things. They tested it on fair machine learning and showed that it works well. |
Keywords
» Artificial intelligence » Machine learning
