Causal Effect Identification in a Sub-Population with Latent Variables

Autor:	Abouei, Amir Mohammad, Mokhtarian, Ehsan, Kiyavash, Negar, Grossglauser, Matthias
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 19 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.14547 Zobrazit plný text záznamu View this record from Arxiv