Summary of Detection Of Unobserved Common Causes Based on Nml Code in Discrete, Mixed, and Continuous Variables, by Masatoshi Kobayashi et al.
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables
by Masatoshi Kobayashi, Kohei Miyagichi, Shin Matsushima
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); 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 This paper tackles the crucial problem of causal discovery from observational data only, in the presence of unobserved common causes. The authors categorize all possible causal relationships between two random variables into four categories: direct causality, independence, confounding by latent confounders, and propose a method to identify one of these cases without requiring assumptions on the form of equation models. Building upon their previous work, CLOUD (Kobayashi et al., 2022), this paper extends CLOUD to apply to various data types, including discrete, mixed, and continuous. The authors demonstrate the consistency and effectiveness of CLOUD through theoretical analysis and extensive experiments on both synthetic and real-world data. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal discovery is a way to figure out if one thing causes another just by looking at how they behave together. This paper helps with that problem when there’s something unseen causing things to happen. The authors sort possible relationships between two things into four groups: one thing causing the other, not being related, or being mixed up because of something else. They already had a method called CLOUD that didn’t need special assumptions and now they’re making it work with different types of data. They also tested it and showed it works well. |