Summary of Partial Structure Discovery Is Sufficient For No-regret Learning in Causal Bandits, by Muhammad Qasim Elahi et al.
Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
by Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu
First submitted to arxiv on: 6 Nov 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 Medium Difficulty summary: This paper focuses on the causal bandit problem, where an optimal decision is learned by understanding relationships between variables and a reward variable. Current approaches assume a known causal graph, but this may not be available beforehand. The authors propose a novel approach to tackle this challenge, where they develop a randomized algorithm for learning the causal graph with limited samples. They also introduce a two-stage method for the causal bandit setup: first, learn the induced subgraph on ancestors of the reward and necessary latent confounders; then apply a standard bandit algorithm like UCB. This approach provides a sample complexity guarantee and regret bounds that scale polynomially or sublinearly with respect to the number of nodes in the causal graph. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you’re trying to make good decisions, but you don’t know exactly how different factors affect each other. This paper helps solve this problem by developing a new way to figure out these relationships when we don’t have all the information upfront. They show that by learning about these relationships in two stages, they can make better decisions and minimize mistakes. The authors also prove that their method works well with limited data. |
