Summary of Model-agnostic Meta-learners For Estimating Heterogeneous Treatment Effects Over Time, by Dennis Frauen et al.
Model-agnostic meta-learners for estimating heterogeneous treatment effects over time
by Dennis Frauen, Konstantin Hess, Stefan Feuerriegel
First submitted to arxiv on: 7 Jul 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 proposed paper introduces several model-agnostic learners, known as meta-learners, that can be used in conjunction with arbitrary machine learning models to estimate heterogeneous treatment effects (HTEs) over time. The focus is on weighted pseudo-outcome regressions, which allows for efficient estimation by targeting the treatment effect directly. Theoretical analysis characterizes the different learners and provides insights into when specific learners are preferable. Numerical experiments confirm the theoretical findings. This work proposes a comprehensive set of meta-learners for estimating HTEs in the time-varying setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to personalize treatment decisions based on electronic health records collected over time. Currently, there aren’t many methods that can do this while also working with different machine learning models. The researchers propose new approaches called meta-learners that can be used with any type of model. They show that these approaches are effective and provide insights into when one approach is better than another. |
Keywords
» Artificial intelligence » Machine learning
