Summary of Causal Effect Estimation Using Identifiable Variational Autoencoder with Latent Confounders and Post-treatment Variables, by Yang Xie et al.
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
by Yang Xie, Ziqi Xu, Debo Cheng, Jiuyong Li, Lin Liu, Yinghao Zhang, Zaiwen Feng
First submitted to arxiv on: 13 Aug 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 proposed method, CPTiVAE, is a novel approach for estimating causal effects from observational data that takes into account both latent confounders and post-treatment variables. By combining Variational AutoEncoders (VAEs) and identifiable VAEs, CPTiVAE learns representations of these variables from their proxy variables, enabling unbiased causal effect estimation. The method is demonstrated to outperform state-of-the-art approaches on synthetic and semi-synthetic datasets, and its potential application is shown using a real-world dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal effects from observational data are hard to estimate because of hidden factors. Many solutions ignore the bias caused by things that happen after an event. This paper proposes a new way to learn about these hidden factors and their relationship to events that come later. It combines two types of machines learning models, VAEs and iVAEs, to create CPTiVAE. This method is tested on fake and real data and shows better results than other approaches. |
