Summary of Local Causal Structure Learning in the Presence Of Latent Variables, by Feng Xie et al.
Local Causal Structure Learning in the Presence of Latent Variables
by Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng
First submitted to arxiv on: 25 May 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 This paper tackles the challenge of discovering causal relationships from observational data when latent variables are present. Current local structure learning methods assume causal sufficiency, but this is often violated in real-world applications, leading to inaccurate structures. The authors propose a new method that harnesses causal information from m-separation and V-structures to derive theoretical consistency results, bridging the gap between global and local structure learning. They also introduce stop rules for determining whether a variable is a direct cause or effect of a target. Under standard causal Markov and faithfulness conditions, the authors theoretically demonstrate the correctness of their approach with infinite samples. Experimental results on synthetic and real-world data validate the effectiveness and efficiency of their method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how to find connections between things that happen in the world from just looking at what happens. It’s hard when there are hidden factors causing some of those events, but current methods don’t account for this. The authors developed a new way to figure out which events cause or are caused by each other. They used special mathematical concepts and tested their method on fake and real data to see how well it worked. |
