Summary of Estimating Conditional Average Treatment Effects Via Sufficient Representation Learning, by Pengfei Shi et al.
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning
by Pengfei Shi, Wei Zhong, Xinyu Zhang, Ningtao Wang, Xing Fu, Weiqiang Wang, Yin Jin
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel neural network approach, CrossNet, is proposed to estimate conditional average treatment effects (CATE) in high-dimensional data. The unconfoundedness assumption is typically required for identifiability, but existing methods do not verify whether the selected variables or learned representations satisfy this assumption. To address this issue, CrossNet learns a sufficient representation from features and then estimates CATE using both within-group and cross-group data. This approach outperforms competitive methods in numerical simulations and empirical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CrossNet is a new way to find treatment effects in big data. Normally, we need to assume that the data is unconfounded, but this can be hard to check. CrossNet learns how to represent the data so that it’s easier to estimate the treatment effects. It uses both the same-group and different-group data to make more accurate predictions. |
Keywords
» Artificial intelligence » Neural network
