Summary of Causal Discovery in Linear Models with Unobserved Variables and Measurement Error, by Yuqin Yang et al.
Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
by Yuqin Yang, Mohamed Nafea, Negar Kiyavash, Kun Zhang, AmirEmad Ghassami
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 proposed study addresses two significant challenges in causal structure learning: unobserved common causes and measurement error. The authors examine linear models with four types of variables, considering scenarios where these challenges coexist. They explore identifiability under separability conditions and faithfulness assumptions, introducing a notion of observational equivalence. A graphical characterization is provided for equivalent models, along with a recovery algorithm that can retrieve the ground truth. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to tackle two major obstacles in understanding cause-and-effect relationships: hidden common causes and measurement mistakes. By studying linear models with different types of variables, researchers hope to better understand how these challenges interact and affect our ability to detect true causal connections. The paper discusses ways to identify the underlying truth despite these issues and proposes a method for retrieving it. |
