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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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GrooveSquid.com Paper Summaries

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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