Summary of Causal Effect Identification in a Sub-population with Latent Variables, by Amir Mohammad Abouei et al.
Causal Effect Identification in a Sub-Population with Latent Variables
by Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash, Matthias Grossglauser
First submitted to arxiv on: 23 May 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 The proposed paper tackles an extension of the Structural Causal Inference (s-ID) problem that allows for the presence of latent variables in observational data from a specific sub-population. The goal is to compute causal effects within this sub-population, building upon classical relevant graphical definitions such as c-components and Hedges. To address the challenges induced by latent variables, the authors extend these definitions and propose a sound algorithm for solving the s-ID problem with latent variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a tricky problem in machine learning called structural causal inference (s-ID) when there are hidden variables. Imagine trying to figure out how a new medicine will work on people who have certain underlying health conditions, but you only know about those conditions from observing how they affect the outcome of some measurements. The authors develop a way to solve this problem by extending some existing ideas and creating a new algorithm. |
Keywords
» Artificial intelligence » Inference » Machine learning
