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Computer Science > Machine Learning

arXiv:2405.14547 (cs)
[Submitted on 23 May 2024 (v1), last revised 29 Oct 2024 (this version, v2)]

Title:Causal Effect Identification in a Sub-Population with Latent Variables

Authors:Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash, Matthias Grossglauser
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Abstract:The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023). This problem has been addressed when all the variables in the system are observable. In this paper, we consider an extension of the s-ID problem that allows for the presence of latent variables. To tackle the challenges induced by the presence of latent variables in a sub-population, we first extend the classical relevant graphical definitions, such as c-components and Hedges, initially defined for the so-called ID problem (Pearl, 1995; Tian & Pearl, 2002), to their new counterparts. Subsequently, we propose a sound algorithm for the s-ID problem with latent variables.
Comments: 28 pages, 7 figures
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as: arXiv:2405.14547 [cs.LG]
  (or arXiv:2405.14547v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2405.14547

Submission history

From: Amir Mohammad Abouei [view email]
[v1] Thu, 23 May 2024 13:25:41 UTC (287 KB)
[v2] Tue, 29 Oct 2024 11:15:37 UTC (296 KB)
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