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 [AMK24]. 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 [Pea95, TP02], to their new counterparts. Subsequently, we propose a sound algorithm for the S-ID problem with latent variables.