Summary of Missnodag: Differentiable Cyclic Causal Graph Learning From Incomplete Data, by Muralikrishnna G. Sethuraman et al.
MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
by Muralikrishnna G. Sethuraman, Razieh Nabi, Faramarz Fekri
First submitted to arxiv on: 24 Oct 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 proposes a novel framework called MissNODAG for discovering causal relationships in complex systems, such as biological networks, where feedback loops and incomplete data are prevalent. The framework integrates an additive noise model with an expectation-maximization procedure to simultaneously learn both the underlying cyclic causal graph and the missingness mechanism from partially observed data. The method can handle missing values not at random, a common issue in real-world datasets. MissNODAG is demonstrated to be effective through synthetic experiments and a case study on real-world gene perturbation data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out how different things are connected in a complex system, like a biological network. But what if some of the connections are missing or hidden? That’s where this new method comes in. It can help us discover the underlying relationships between things even when there’s incomplete data and feedback loops are present. This is important because it allows us to better understand how systems work and make more accurate predictions. The method has been tested on fake data and real-world gene perturbation data, showing promising results. |
