Summary of Geometry-aware Instrumental Variable Regression, by Heiner Kremer et al.
Geometry-Aware Instrumental Variable Regression
by Heiner Kremer, Bernhard Schölkopf
First submitted to arxiv on: 19 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 Sinkhorn Method of Moments is an optimal transport-based instrumental variable (IV) regression estimator that addresses the limitations of traditional IV methods. By incorporating data-derivative information, this approach provides a more robust estimate of the population data distribution, even in the presence of corrupted or adversarial data. The proposed method is simple to implement and performs similarly to existing estimators in standard settings while showing improved resilience against data corruption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Sinkhorn Method of Moments is a new way to do instrumental variable regression. It’s better than old methods because it takes into account how the data is related to each other. This helps it work well even when some of the data is fake or trying to trick it. The method is easy to use and works just as well as older methods in normal situations, but is much more robust against bad data. |
Keywords
» Artificial intelligence » Regression
