Establishing causal claims is one of the primary endeavors in sociological research. Statistical causal inference is a promising way to achieve this through the potential outcome framework or structural causal models, which are based on a set of identification assumptions. However, identification assumptions are often not fully discussed in practice, which harms the validity of causal claims. In this article, we focus on the unmeasurededness assumption that assumes no unmeasured confounders in models, which is often violated in practice. This article reviews a set of papers in two leading sociological journals to check the practice of causal inference and relevant identification assumptions, indicating the lack of discussion on sensitivity analysis methods on unconfoundedness in practice. And then, a blueprint of how to conduct sensitivity analysis methods on unconfoundedness is built, including six steps of proper choices on practices of sensitivity analysis to evaluate the impacts of unmeasured confounders.

翻译：建立因果主张是社会学研究的主要目标之一。通过潜在结果框架或结构因果模型（基于一组识别假设）进行统计因果推断，是实现这一目标的有前景的路径。然而，在实践中，识别假设往往未被充分讨论，这损害了因果主张的有效性。本文聚焦于无混杂假设（即假设模型中不存在未测量的混杂因素），而这一假设在实践中经常被违反。文章通过综述两大顶级社会学期刊中的一组论文，审视了因果推断及相关识别假设的实践现状，指出实践中缺乏对无混杂性的敏感性分析方法的讨论。进而，构建了开展无混杂性敏感性分析的实施蓝图，包括评估未测量混杂因素影响的六个关键步骤。