Summary of Efficient Differentiable Discovery Of Causal Order, by Mathieu Chevalley et al.
Efficient Differentiable Discovery of Causal Order
by Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed Intersort algorithm discovers the causal order of variables in a Directed Acyclic Graph (DAG) model by leveraging interventional data, outperforming existing methods. However, it is computationally expensive and non-differentiable, limiting its application to large-scale datasets or integration into gradient-based learning frameworks. Our approach reformulates Intersort using differentiable sorting and ranking techniques, enabling scalable and differentiable optimization of causal orderings. This allows the continuous score function to be incorporated as a regularizer in downstream tasks. Empirical results show that causal discovery algorithms benefit significantly from regularizing on the causal order. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to find the correct order of variables in a graph model using data from experiments. The current method, Intersort, is good but slow and hard to use with big datasets. To fix this, the researchers developed a new version that uses sorting and ranking techniques to make it faster and easier to use. This allows the score function to be used as a guide in other tasks. The results show that finding the correct order helps improve the accuracy of algorithms. |
Keywords
* Artificial intelligence * Optimization
