Summary of Regularized Deepiv with Model Selection, by Zihao Li et al.
Regularized DeepIV with Model Selection
by Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang, Masatoshi Uehara
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Econometrics (econ.EM); Statistics Theory (math.ST); 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 paper presents Regularized DeepIV (RDIV) regression, a minimax-oracle-free method for nonparametric estimation of instrumental variable (IV) regressions. RDIV avoids three common limitations of existing methods: uniquely identified IV regression, requiring minimax computation oracle, and absence of model selection procedure. The approach consists of two stages: learning the conditional distribution of covariates and using this distribution to learn the estimator by minimizing a Tikhonov-regularized loss function. The method allows model selection procedures that achieve oracle rates in the misspecified regime. When extended to an iterative estimator, RDIV matches the current state-of-the-art convergence rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in machine learning called instrumental variable regression. It’s like trying to figure out what makes something happen, but you can only see some of the clues. The researchers created a new way to do this that doesn’t have some of the usual problems with other methods. This new method is called Regularized DeepIV (RDIV). It works by first learning about how things are related, and then using that information to find the right answer. This method can even choose which clues to use, which helps it work better when the clues aren’t perfect. |
Keywords
* Artificial intelligence * Loss function * Machine learning * Regression
