Summary of Scalable Differentiable Causal Discovery in the Presence Of Latent Confounders with Skeleton Posterior (extended Version), by Pingchuan Ma et al.
Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
by Pingchuan Ma, Rui Ding, Qiang Fu, Jiaru Zhang, Shuai Wang, Shi Han, Dongmei Zhang
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 Differentiable causal discovery has made significant advancements in learning directed acyclic graphs. However, its application to real-world datasets remains restricted due to the presence of latent confounders and the requirement to learn maximal ancestral graphs (MAGs). This paper proposes a novel approach to overcome these limitations. The method, which is based on a differentiable version of the MAG algorithm, allows for the learning of larger datasets with more than 50 variables. Evaluation metrics such as the Matthews correlation coefficient and the mean squared error are used to benchmark the performance of the proposed model on several real-world datasets. The paper demonstrates the effectiveness of the approach in uncovering causal relationships between variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find the causes behind things we observe in the world. Right now, it’s hard to use this method with big datasets because there are hidden factors that affect what we see. The scientists propose a new approach that can handle these hidden factors and work with bigger datasets. They tested their method on real-world data and showed that it can find the right causes behind things. |
