Summary of Recovering Latent Confounders From High-dimensional Proxy Variables, by Nathan Mankovich et al.
Recovering Latent Confounders from High-dimensional Proxy Variables
by Nathan Mankovich, Homer Durand, Emiliano Diaz, Gherardo Varando, Gustau Camps-Valls
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces the Proxy Confounder Factorization (PCF) framework for estimating causal effects when proxy variables are high-dimensional and mixed. This approach removes previous assumptions about low-dimensional or sorted proxies and binary treatments. The authors present two implementations of PCF: ICA-PCF, which uses Independent Component Analysis, and GD-PCF, which employs Gradient Descent. The methods achieve high correlation with the latent confounder and low absolute error in causal effect estimation using synthetic datasets. Even when applied to climate data, ICA-PCF recovers four components that explain a significant portion of variance in the North Atlantic Oscillation, a known confounder of precipitation patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding hidden causes in big data sets. Scientists have been trying to figure out how to do this for years, but it’s a hard problem because usually we only have clues (proxy variables) that are not very good at telling us what the real cause is. The new approach, called PCF, makes some big improvements and can be used in many different fields like climate science. |
Keywords
* Artificial intelligence * Gradient descent
