Summary of Adaptive Online Experimental Design For Causal Discovery, by Muhammad Qasim Elahi et al.
Adaptive Online Experimental Design for Causal Discovery
by Muhammad Qasim Elahi, Lai Wei, Murat Kocaoglu, Mahsa Ghasemi
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 paper proposes a novel approach to causal discovery, which aims to uncover cause-and-effect relationships encoded in causal graphs using observational, interventional data, or their combination. By formalizing causal discovery from an online learning perspective, inspired by pure exploration in bandit problems, the authors develop a track-and-stop algorithm that adaptively selects interventions and learns the causal graph based on sampling history. The proposed algorithm outperforms existing methods in simulations across various randomly generated causal graphs, achieving higher accuracy with significantly fewer samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding cause-and-effect relationships between things, like what makes something happen. Right now, most ways to do this assume you have a lot of data that shows what would have happened if certain things didn’t happen. But the authors are looking at how to do it with less data. They came up with an algorithm that can find these relationships by trying out different interventions and learning from them. It’s better than other methods at finding the right answers with fewer tries. |
Keywords
» Artificial intelligence » Online learning
