Summary of Scalable and Flexible Causal Discovery with An Efficient Test For Adjacency, by Alan Nawzad Amin and Andrew Gordon Wilson
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
by Alan Nawzad Amin, Andrew Gordon Wilson
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 presents a scalable and flexible method to evaluate if two variables are adjacent in a causal graph, called Differentiable Adjacency Test (DAT). DAT replaces an exponential number of tests with a provably equivalent relaxed problem, which is solved by training two neural networks. This approach enables the construction of a graph learning method based on DAT, called DAT-Graph, that can learn graphs of 1000 variables with state-of-the-art accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions and understand complex systems by learning causal graphs from large datasets. The goal is to find the best fit between data and the graph, but this task is very difficult because there are many possible graphs. To solve this problem, the researchers created a new method called Differentiable Adjacency Test (DAT). DAT works by training two neural networks to test if two variables are connected in a causal graph. This allows us to learn graphs with 1000 variables accurately and make predictions about how interventions will affect complex systems. |
