Summary of Dynamical Causality Under Invisible Confounders, by Jinling Yan et al.
Dynamical causality under invisible confounders
by Jinling Yan, Shao-Wu Zhang, Chihao Zhang, Weitian Huang, Jifan Shi, Luonan Chen
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 proposed method, known as the Causality Inference under Invisible Confounders (CIC) method, addresses the challenge of accurately inferring causation in complex systems where invisible/unobservable confounders are present. By decomposing observed variables into common and private subspaces using an orthogonal decomposition theorem in a delay embedding space, the CIC method enables causal detection for high-dimensional systems even with only two observed variables under many invisible confounders. This approach also solves the non-separability problem of causal inference by making intertwined variables separable in the embedding space. The effectiveness of the CIC method is demonstrated through extensive validation using various real datasets, including the reconstruction of real biological networks with unobserved confounders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to figure out if one thing causes another, even when there are things we can’t see or measure that might be confusing us. Right now, it’s hard to do this accurately, especially in complex systems where many invisible factors could be influencing the results. The researchers developed a method called CIC (Causality Inference under Invisible Confounders) that can help solve this problem. It works by breaking down what we see into different parts and then using those parts to understand how things are related. This helps us get more accurate answers, even in situations where there are many invisible factors at play. |
Keywords
* Artificial intelligence * Embedding space * Inference
