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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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GrooveSquid.com Paper Summaries

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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.

Keywords

* Artificial intelligence