Summary of Stochastic Optimization Algorithms For Instrumental Variable Regression with Streaming Data, by Xuxing Chen et al.
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
by Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Econometrics (econ.EM); Optimization and Control (math.OC)
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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 presents algorithms for instrumental variable regression by reframeing it as a conditional stochastic optimization problem. The developed methods can perform instrumental variable regression with streaming data without requiring matrix inversions or mini-batches, making them fully online and efficient. When the true model is linear, the algorithms achieve convergence rates of order O(log T/T) and O(1/T^(1-i)) for two-sample and one-sample oracles, respectively. This approach avoids modeling the relationship between confounder and instrumental variables, outperforming recent minimax optimization-based methods. Numerical experiments support the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new ways to do a type of statistical analysis called instrumental variable regression. It’s like solving a puzzle with data that helps us understand how things are related. The authors came up with faster and more efficient methods for doing this analysis, even when we have lots of data coming in all at once. They also showed that their approach is better than some other ways people have tried to do this type of analysis. |
Keywords
» Artificial intelligence » Optimization » Regression
