Summary of Learning Cyclic Causal Models From Incomplete Data, by Muralikrishnna G. Sethuraman et al.
Learning Cyclic Causal Models from Incomplete Data
by Muralikrishnna G. Sethuraman, Faramarz Fekri
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a novel framework called MissNODAGS for learning cyclic causal graphs from partially missing data. The existing algorithms in this field operate under the assumptions of an acyclic graph and complete data, which can be problematic in real-world scenarios where feedback loops are present and data is often incomplete. MissNODAGS uses the additive noise model and alternates between imputing missing data and maximizing the log-likelihood to learn the causal graph, leveraging the principles of expectation-maximization (EM) framework. The approach demonstrates improved performance compared to state-of-the-art methods on both synthetic and real-world single-cell perturbation data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in science: figuring out how things will change if we do something new. It’s like trying to predict what will happen if you give medicine to someone who has an illness, but the problem is that there might be missing information or loops in the system. The researchers created a new way called MissNODAGS to deal with these issues and learn about the relationships between things. They tested it on made-up data and real-world examples from biology and found that it works better than other methods. |
Keywords
* Artificial intelligence * Log likelihood
