This article contributes to the discussion on the relationship between the Neyman-Rubin and the graphical frameworks for causal inference. We present specific examples of data-generating mechanisms - such as those involving undirected or deterministic relationships and cycles - where analyses using a directed acyclic graph are challenging, but where the tools from the Neyman-Rubin causal framework are readily applicable. We also provide examples of data-generating mechanisms with M-bias, trapdoor variables, and complex front-door structures, where the application of the Neyman-Rubin approach is complicated, but the graphical approach is directly usable. The examples offer insights into commonly used causal inference frameworks and aim to improve comprehension of the languages for causal reasoning among a broad audience.

翻译：本文旨在探讨Neyman-Rubin因果框架与图因果框架在因果推断中的关系。我们通过具体的数据生成机制案例——例如涉及无向或确定性关系及循环结构的场景——说明在基于有向无环图的分析面临挑战时，Neyman-Rubin因果框架的工具能直接适用。同时，我们展示了存在M偏倚、陷阱门变量及复杂前门结构的数据生成机制案例，其中Neyman-Rubin方法的应用较为复杂，而图方法则能直接应用。这些案例为理解常用因果推断框架提供了新视角，旨在提升广大研究者对因果推理语言体系的认知。