Summary of Core: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning, by Andreas W.m. Sauter et al.
CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
by Andreas W.M. Sauter, Nicolò Botteghi, Erman Acar, Aske Plaat
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 CORE, a deep reinforcement learning-based approach for causal discovery and intervention planning. Building on Pearl’s Causal Hierarchy, which emphasizes the importance of interventions in distinguishing correlation from causation, CORE learns to sequentially reconstruct causal graphs from data while learning to perform informative interventions. The approach generalizes well to unseen graphs, efficiently uncovers causal structures, and outperforms existing methods in terms of structure estimation accuracy and sample efficiency. The paper demonstrates CORE’s scalability to larger graphs with up to 10 variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CORE is a new way to figure out how things are related when we don’t know what’s causing certain events. It uses computer learning to find patterns in data, but also lets us control certain things to see how they affect the results. This helps us learn more about why things happen and makes it easier to predict future events. The researchers showed that CORE is good at finding causal relationships and can handle bigger datasets than other methods. |
Keywords
* Artificial intelligence * Reinforcement learning
