Summary of Marrying Causal Representation Learning with Dynamical Systems For Science, by Dingling Yao et al.
Marrying Causal Representation Learning with Dynamical Systems for Science
by Dingling Yao, Caroline Muller, Francesco Locatello
First submitted to arxiv on: 22 May 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 The proposed method combines the strengths of causal representation learning and dynamical systems, allowing for identifiable models that can be scaled up to real-world applications. By leveraging scalable differentiable solvers developed for differential equations, the approach learns explicitly controllable models that isolate trajectory-specific parameters for downstream tasks such as out-of-distribution classification or treatment effect estimation. The method is demonstrated on a wind simulator with partially known factors of variation and applied to real-world climate data, successfully answering causal questions in line with existing literature on climate change. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal representation learning helps us understand hidden causes from messy measurements. But most work focuses on proving it works, not using it for real-world problems. Meanwhile, dynamical systems can learn complex patterns but don’t let us figure out what’s causing them. This paper connects the two and shows how to use identifiable methods developed for causal representation learning in dynamical systems. It also uses scalable solvers from differential equations to build models that are both useful and accurate. The result is a way to isolate specific factors and answer important questions, like how climate change will affect different regions. |
Keywords
» Artificial intelligence » Classification » Representation learning
