Summary of Automating the Selection Of Proxy Variables Of Unmeasured Confounders, by Feng Xie et al.
Automating the Selection of Proxy Variables of Unmeasured Confounders
by Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, Zhi Geng
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 proposes new methods for estimating causal effects in the presence of unmeasured confounders, a common challenge in observational studies. It addresses this issue by extending existing proxy variable estimators to handle multiple unobserved confounders and providing identifiability conditions for selecting valid proxies. The authors present two data-driven methods for selecting proxies and estimating causal effects. Experimental results on synthetic and real-world data demonstrate the effectiveness of their approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in science by helping us understand what’s happening when we can’t measure everything. It shows how to figure out what’s causing things to happen even if there are some things we don’t know about. The authors have new ways to deal with this tricky situation, and they test their ideas on real data. |
