Summary of Learning Directed Acyclic Graphs From Partial Orderings, by Ali Shojaie and Wenyu Chen
Learning Directed Acyclic Graphs from Partial Orderings
by Ali Shojaie, Wenyu Chen
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 Directed acyclic graphs (DAGs) are crucial in modeling causal relationships among variables, but learning their structure is computationally and statistically demanding. In contrast, when a complete causal ordering of variables is available, the problem becomes tractable even in high dimensions. This paper tackles the intermediate problem of learning DAGs with partial causal ordering information. We propose an estimation framework that leverages this partial ordering and develop efficient algorithms for low- and high-dimensional problems. Our approach’s benefits are demonstrated through numerical studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding patterns in complex data relationships. Usually, it’s hard to figure out how variables are connected just by looking at the data. But what if we had some extra information that helps us understand the order of these connections? This paper shows how we can use this extra information to make the problem easier to solve, even when dealing with lots of variables. We developed a new way to do this and tested it with examples. |
