Single-case experimental designs (SCEDs) have a long history in clinical and educational disciplines. One underdeveloped area in advancing SCED design and analysis is understanding the process of how internal validity threats and operational concerns are avoided or mitigated. Two strategies to ameliorate such issues in SCED involve replication and randomization. Although replication and randomization are indispensable tools in improving the internal validity of SCEDs, little attention has been paid to (a) why this is the case; or (b) the ways in which these design features are not immune from internal validity threats and operational concerns. In the current paper, we describe the use of directed acyclic graphs (DAGs) to better understand, discover, and mitigate internal validity threats and operational concerns in SCEDs. DAGs are a tool for visualizing causal relations among variables and can help researchers identify both causal and noncausal relations among their variables according to specific algorithms. We introduce the use of DAGs in SCEDs to prompt applied researchers to conceptualize internal validity threats and operational concerns, even when an SCED includes replication and randomization in the design structure. We discuss the general principles of causal inference in conventional “group” designs and in SCEDs, the unique factors impacting SCEDs, and how DAGs can be incorporated into SCEDs. We also discuss the limitations of DAGs applied to SCEDs, as well as future directions for this area of work.

单案例实验设计 （SCED） 在临床和教育学科中有着悠久的历史。推进 SCED 设计和分析的一个未开发领域是了解如何避免或减轻内部有效性威胁和运营问题的过程。改善 SCED 中此类问题的两种策略包括复制和随机化。尽管复制和随机化是提高 SCED 内部有效性不可或缺的工具，但很少关注 （a） 为什么会这样;或 （b） 这些设计功能无法避免内部有效性威胁和操作问题的方式。在本文中，我们描述了有向无环图 （DAG） 的使用，以更好地理解、发现和减轻 SCED 中的内部有效性威胁和操作问题。DAG 是一种可视化变量间因果关系的工具，可以帮助研究人员根据特定算法识别变量之间的因果关系和非因果关系。我们介绍了 SCED 中 DAG 的使用，以促使应用研究人员概念化内部有效性威胁和运营问题，即使 SCED 在设计结构中包括复制和随机化。我们讨论了传统“组”设计和 SCED 中因果推理的一般原则、影响 SCED 的独特因素，以及如何将 DAG 纳入 SCED。我们还讨论了应用于 SCED 的 DAG 的局限性，以及该工作领域的未来方向。