Summary of Identifiability Guarantees For Causal Disentanglement From Purely Observational Data, by Ryan Welch et al.
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
by Ryan Welch, Jiaqi Zhang, Caroline Uhler
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Causal disentanglement seeks to uncover hidden causal factors behind data, enhancing representation learning methods with interpretability and extrapolation capabilities. Recent breakthroughs have established identifiability results under assumptions of single-latent-factor interventions; however, the feasibility of these assumptions remains uncertain due to the inherent complexity of intervening on latent variables. In response, we re-examine the fundamentals and explore what can be learned using solely observational data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding the underlying causes behind the data we see. It’s like trying to figure out why people behave in certain ways based on their environment. The current methods for doing this require making some assumptions that might not always hold true. So, researchers are asking if it’s possible to learn more without those assumptions. They want to know what they can discover just by looking at the data, without having to make any changes or interventions. |
Keywords
» Artificial intelligence » Representation learning
