Summary of Transformers Handle Endogeneity in In-context Linear Regression, by Haodong Liang et al.
Transformers Handle Endogeneity in In-Context Linear Regression
by Haodong Liang, Krishnakumar Balasubramanian, Lifeng Lai
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Econometrics (econ.EM); Statistics Theory (math.ST)
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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 A novel approach to addressing endogeneity in linear regression is proposed using transformers. By leveraging instrumental variables (IV), the transformer architecture can effectively handle endogeneity issues. The study demonstrates that the transformer can emulate a gradient-based bi-level optimization procedure, converging to the widely used two-stage least squares (2SLS) solution at an exponential rate. Additionally, an in-context pretraining scheme is proposed, backed by theoretical guarantees showing that the global minimizer of the pre-training loss achieves a small excess loss. Experimental results validate these findings, revealing that the trained transformer provides more robust and reliable in-context predictions and coefficient estimates than the 2SLS method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are really good at something called linear regression. Linear regression is a way to predict a value based on some input values. But sometimes, there’s an issue called endogeneity, which can make the predictions not very accurate. The researchers in this paper found that transformers have a built-in mechanism to handle endogeneity using something called instrumental variables (IV). They showed that the transformer can learn to optimize its own performance by pretending it’s doing two-stage least squares (2SLS), which is a widely used method for handling endogeneity. Then, they proposed a new way of pretraining the transformer and proved that it works well. The results show that the trained transformer makes better predictions than the traditional 2SLS method. |
Keywords
» Artificial intelligence » Linear regression » Optimization » Pretraining » Transformer
