As any scientific discipline, the software engineering (SE) research community strives to contribute to the betterment of the target population of our research: software producers and consumers. We will only achieve this betterment if we manage to transfer the knowledge acquired during research into practice. This transferal of knowledge may come in the form of tools, processes, and guidelines for software developers. However, the value of these contributions hinges on the assumption that applying them causes an improvement of the development process, user experience, or other performance metrics. Such a promise requires evidence of causal relationships between an exposure or intervention (i.e., the contributed tool, process or guideline) and an outcome (i.e., performance metrics). A straight-forward approach to obtaining this evidence is via controlled experiments in which a sample of a population is randomly divided into a group exposed to the new tool, process, or guideline, and a control group. However, such randomized control trials may not be legally, ethically, or logistically feasible. In these cases, we need a reliable process for statistical causal inference (SCI) from observational data.

翻译：如同任何科学学科一样，软件工程（SE）研究界致力于推动研究目标群体——软件生产者与消费者——的进步。唯有将研究过程中获得的知识有效转化为实践，我们才能实现这一进步。这种知识转化可能以工具、流程和指导原则的形式呈现给软件开发人员。然而，这些贡献的价值取决于一个基本假设：应用它们能够对开发流程、用户体验或其他性能指标产生改进作用。此类承诺需要提供暴露或干预（即所贡献的工具、流程或指导原则）与结果（即性能指标）之间因果关系的证据。获取此类证据的直接方法是通过受控实验，将总体样本随机分为接受新工具、流程或指导原则的暴露组和对照组。然而，此类随机对照试验可能在法律、伦理或操作层面不可行。在此类情况下，我们需要一个从观测数据中进行统计因果推断（SCI）的可靠流程。