Summary of Generalization Of Lingam That Allows Confounding, by Joe Suzuki and Tian-le Yang
Generalization of LiNGAM that allows confounding
by Joe Suzuki, Tian-Le Yang
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Statistics Theory (math.ST)
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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 introduces LiNGAM-MMI, a method that enhances the original LiNGAM algorithm to determine the variable order from cause to effect while addressing confounding. Unlike previous methods that maintained LiNGAM’s structure, LiNGAM-MMI quantifies the magnitude of confounding using KL divergence and arranges variables to minimize its impact. This allows for efficient processing of data, even in scenarios with confounding. The method formulates the shortest path problem to achieve a globally optimal variable order. Experimental results show that LiNGAM-MMI accurately determines the correct variable order, both with and without confounding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LiNGAM is an algorithm that tries to figure out what happens first and what happens next in a chain of events. But sometimes, there are hidden problems called “confounding” that can mess up this process. Previous methods tried to fix these issues but were slow and didn’t work well for all types of confounding. This new method, LiNGAM-MMI, is faster and more effective at finding the correct order of events even when there’s confounding involved. It does this by measuring how much confounding is present and rearranging things to minimize its impact. The results show that this new method is better than the original one in both cases. |
