一句话：论文主张仅靠数据分析不足以揭示可靠因果关系，必须辅以恰当的研究设计，并梳理因果推断所需条件、观察性研究的挑战及应对机制。
该文是一篇立场论文，讨论了从观察数据中识别与估计因果关系这一任务。作者指出，因果推断方法在多个研究领域已展现出从观察数据中发现因果关系的潜力，近年来在软件工程中也受到越来越多的关注；然而，用观察性数据探究因果关系本身充满困难，仅依赖数据分析无法保证结论可靠。为此，论文系统阐述了揭示因果关系所需满足的基本条件，分析了观察性研究在满足这些条件时面临的挑战（如混杂、选择偏差等），并讨论了克服这些挑战的机制，核心论点是：可靠因果关系的发现始终需要恰当的研究设计，而不仅是事后的分析方法。论文以概念论证与文献综合为主，并未报告新的实证评估。
In a variety of research fields, causal inference methods have demonstrated potential benefits in identifying and estimating causal relationships from observational data. These methods have started to receive increasing attention in software engineering as well. However, the investigation of causal relationships can be challenging when dealing with observational data. In this paper, we uphold the position that data analysis alone is not enough and a proper design is always needed to detect reliable causal relationships. To this end, we describe the fundamental conditions needed to reveal causal relationships, the challenges of observational studies in satisfying them, and the mechanisms to overcome those challenges.