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.

翻译：s-ID问题旨在从同一子总体的观测数据中计算该特定子总体内的因果效应（Abouei等人，2023）。该问题在系统中所有变量均可观测的情况下已得到解决。本文考虑了s-ID问题的一个扩展，允许潜在变量的存在。为应对子总体中潜在变量带来的挑战，我们首先将经典的相关图定义（如c-分量和Hedges，最初为所谓的ID问题定义（Pearl, 1995; Tian & Pearl, 2002））扩展至其新的对应形式。随后，我们提出了一种针对含潜在变量的s-ID问题的可靠算法。