Summary of Nonparametric Instrumental Variable Regression Through Stochastic Approximate Gradients, by Yuri Fonseca et al.
Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
by Yuri Fonseca, Caio Peixoto, Yuri Saporito
First submitted to arxiv on: 8 Feb 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 presents a novel approach to instrumental variables (IVs) for identifying causal effects in nonparametric settings. The authors develop a functional stochastic gradient descent algorithm that directly minimizes the populational risk, offering theoretical support through bounds on the excess risk. Numerical experiments demonstrate the method’s stability and competitive performance compared to current state-of-the-art alternatives. The approach allows for flexible estimator choices, such as neural networks or kernel-based methods, and non-quadratic loss functions, making it suitable for structural equations with binary outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to use instrumental variables (IVs) to figure out what causes things to happen when we can’t see all the factors involved. The researchers came up with a new way to do this by using an algorithm that directly tries to minimize errors. They showed that their method works well in tests and is flexible enough to handle different types of data and problems. |
Keywords
* Artificial intelligence * Stochastic gradient descent
