Summary of Spectral Representation For Causal Estimation with Hidden Confounders, by Haotian Sun et al.
Spectral Representation for Causal Estimation with Hidden Confounders
by Haotian Sun, Antoine Moulin, Tongzheng Ren, Arthur Gretton, Bo Dai
First submitted to arxiv on: 15 Jul 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 challenge of estimating causal effects when there are hidden confounders present. Specifically, it focuses on two scenarios: instrumental variable regression with additional observed confounders, and proxy causal learning. The authors propose an approach that involves a singular value decomposition of a conditional expectation operator, followed by a saddle-point optimization problem. This method generalizes the influential work by Darolles et al. [2011] to neural networks. The paper demonstrates the effectiveness of this approach through experimental validation in various settings and shows that it outperforms existing methods on common benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to figure out what’s causing things to happen, even when there are hidden factors at play. They look at two different ways of doing this: one using “instrumental variables” and the other using “proxy causal learning”. The authors come up with a new method that uses math to help us find the right answers. They test their approach and show that it works better than what others have done in similar situations. |
Keywords
* Artificial intelligence * Optimization * Regression
