Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup within a larger population. However, ignoring the subtleties introduced by sub-populations can either lead to erroneous inference or limit the applicability of existing methods. We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population). Existing inference problems in sub-populations operate on the premise that the given data distributions originate from the entire population, thus, cannot tackle the s-ID problem. To address this gap, we provide necessary and sufficient conditions that must hold in the causal graph for a causal effect in a sub-population to be identifiable from the observational distribution of that sub-population. Given these conditions, we present a sound and complete algorithm for the s-ID problem.

翻译：摘要：子群体中的因果推断涉及在更大总体中的特定亚组内识别干预措施的因果效应。然而，忽视子群体引入的微妙之处可能导致错误的推断或限制现有方法的适用性。我们提出并倡导一种子群体中的因果推断问题（以下简称s-ID），在该问题中，我们仅能获取目标子群体的观测数据（而非整个总体的数据）。现有的子群体推断问题基于给定数据分布源自整个总体的前提，因此无法解决s-ID问题。为弥补这一空白，我们提供了因果图中子群体因果效应从该子群体观测分布中可识别必须满足的充要条件。基于这些条件，我们提出了一个针对s-ID问题的完备且可靠的算法。