Summary of Redefining the Shortest Path Problem Formulation Of the Linear Non-gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration, by Hans Jarett J. Ong et al.
Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration
by Hans Jarett J. Ong, Brian Godwin S. Lim, Renzo Roel P. Tan, Kazushi Ikeda
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 A novel approach to causal graph learning from observational data is proposed, addressing limitations in the linear non-Gaussian acyclic model (LiNGAM). By reformulating LiNGAM as a shortest path problem (LiNGAM-SPP), unmeasured confounders can be effectively handled. Mutual information is used to measure independence, but this requires parameter tuning due to its reliance on kNN mutual information estimators. To overcome this challenge, the paper presents a threefold enhancement to the LiNGAM-SPP framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve our ability to learn causal relationships from observational data by reformulating the linear non-Gaussian acyclic model (LiNGAM). The current approach has limitations when it comes to handling unmeasured confounders, but a new method called LiNGAM-SPP can help. This new method uses mutual information to determine if variables are independent, but it requires adjusting some parameters. To make this process easier, the paper suggests three ways to improve the LiNGAM-SPP framework. |
