Summary of Everything That Can Be Learned About a Causal Structure with Latent Variables by Observational and Interventional Probing Schemes, By Marina Maciel Ansanelli et al.
Everything that can be learned about a causal structure with latent variables by observational and interventional probing schemes
by Marina Maciel Ansanelli, Elie Wolfe, Robert W. Spekkens
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Quantum Physics (quant-ph)
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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 explores the limits of distinguishing between different causal structures with latent variables using various probing schemes. It is well-established that many causal structures can produce the same joint probability distributions when observed passively. However, if we can intervene on some variables and observe others, more nuanced differences become apparent. The authors investigate which causal structures remain indistinguishable even under the most informative probing schemes. They find that two causal structures are equivalent if they share the same mDAG structure, as defined by Evans (2016). Furthermore, they examine when one causal structure dominates another, realizing all possible joint probability distributions. Finally, they explore how much the probing scheme can be weakened while still maintaining its discriminatory power. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to figure out which hidden causes make different effects look the same or different. It’s already known that many cause-and-effect relationships can seem the same just by looking at what happens when nothing is done. But if we can try to change some things and see how others respond, it gets more complicated. The researchers want to know which cause-and-effect patterns are truly different even with the best ways of observing or changing things. They find that two patterns are equivalent if they have the same underlying structure. They also look at when one pattern is stronger than another, making all possible effects happen. Finally, they see how much we can simplify our observations while still being able to tell which causes make different effects. |
Keywords
* Artificial intelligence * Probability
