Summary of Disentangled Representation Learning For Causal Inference with Instruments, by Debo Cheng (1) et al.
Disentangled Representation Learning for Causal Inference with Instruments
by Debo Cheng, Jiuyong Li, Lin Liu, Ziqi Xu, Weijia Zhang, Jixue Liu, Thuc Duy Le
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Medium Difficulty summary: This paper addresses the challenge of latent confounders in observational data by relaxing assumptions in instrumental variable (IV) approaches. Traditional IV methods require a known IV or multiple IVs, limiting their applicability. The proposed Variational AutoEncoder (VAE)-based method learns an IV representation from data with latent confounders and utilizes it for unbiased causal effect estimation. Experiments on synthetic and real-world data demonstrate the algorithm’s superiority over existing IV-based and VAE-based estimators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research helps solve a problem in studying cause-and-effect relationships using observational data. The traditional way to do this requires knowing which variable is “instrumental” or has multiple instrumental variables, which isn’t always possible. This paper proposes a new method that uses something called Variational AutoEncoder (VAE) to learn a representation from the data and then use it to get an unbiased estimate of the cause-and-effect relationship. The results show that this new method performs better than other existing methods. |
Keywords
» Artificial intelligence » Variational autoencoder
