Summary of Causal Representation Learning From Multiple Distributions: a General Setting, by Kun Zhang et al.
Causal Representation Learning from Multiple Distributions: A General Setting
by Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng
First submitted to arxiv on: 7 Feb 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 This paper tackles the problem of recovering latent causal variables and their relationships from observed data, with applications in changing environments or making system changes. The task is known as causal representation learning, which assumes that measured variables are mathematical functions of the underlying causes. The authors focus on a nonparametric setting where multiple distributions arise from heterogeneous data or nonstationary time series, without assuming hard interventions behind distribution changes. They develop general solutions and show that under certain conditions, it’s possible to recover the moralized graph of the underlying directed acyclic graph and the relationships between latent variables are related to the underlying causal model in a specific way. The authors also demonstrate experimental results that verify their theoretical claims. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about figuring out what’s really going on behind the scenes by looking at how things change over time or when different data comes in. It’s called “causal representation learning” because we want to understand the underlying causes of what we see, like objects or concepts. The problem is that we only have measurements, like image pixels, and not the actual things themselves. The authors come up with a way to solve this without making too many assumptions about how things work. They show that if certain conditions are met, they can recover some underlying information and understand how it relates to the causes. |
Keywords
* Artificial intelligence * Representation learning * Time series
