Summary of Causal Representation Learning in Temporal Data Via Single-parent Decoding, by Philippe Brouillard et al.
Causal Representation Learning in Temporal Data via Single-Parent Decoding
by Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 tackles the challenge of understanding complex relationships between variables in scientific research, a task known as causal representation learning. The authors propose a novel approach, Causal Discovery with Single-parent Decoding (CDSD), which learns both a mapping to causally-relevant latent variables and a causal model over them. The method relies on a sparsity assumption called single-parent decoding, where each observed variable is influenced by only one underlying latent factor. This assumption is reasonable in many applications, such as climate research or brain activity analysis. The paper demonstrates the identifiability of the proposed model using simulated data and showcases its practical validity in an application to real-world climate science data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists often study complex systems by collecting lots of data. But understanding how these data relate to each other is hard because we can’t see the underlying causes. This paper presents a new way to figure out these relationships, called Causal Discovery with Single-parent Decoding (CDSD). The method assumes that each piece of data is influenced by only one underlying cause, which makes sense for many scientific applications like studying climate or brain activity. The authors show that their approach works well in simulations and real-world climate science data. |
Keywords
» Artificial intelligence » Representation learning
