It has been stated that the notion of cause and effect is one object of study that sciences and engineering revolve around. Lately, in software engineering, diagrammatic causal inference methods (e.g., Pearl s model) have gained popularity (e.g., analyzing causes and effects of change in software requirement development). This paper concerns diagrammatical (graphic) models of causal relationships. Specifically, we experiment with using the conceptual language of thinging machines (TMs) as a tool in this context. This would benefit works on causal relationships in requirements engineering, enhance our understanding of the TM modeling, and contribute to the study of the philosophical notion of causality. To specify the causality in a system s description is to constrain the system s behavior and thus exclude some possible chronologies of events. The notion of causality has been studied based on tools to express causal questions in diagrammatic and algebraic forms. Causal models deploy diagrammatic models, structural equations, and counterfactual and interventional logic. Diagrammatic models serve as a language for representing what we know about the world. The research methodology in the paper focuses on converting causal graphs into TM models and contrasts the two types of representation. The results show that the TM depiction of causality is more complete and therefore can provide a foundation for causal graphs.

翻译：因果关系和因果效应的概念一直是科学与工程研究的核心对象。近年来，在软件工程领域，图解式因果推断方法（如Pearl模型）日益流行（例如，分析软件需求开发中变更的因果影响）。本文关注因果关系的图解（图形化）模型，具体探索将"事物机器"（Thinging Machines, TM）的概念性语言作为该领域的分析工具。这一研究不仅有助于需求工程中因果关系的建模工作，深化对TM建模方法的理解，还能为哲学层面的因果性研究提供新视角。对系统描述中的因果关系进行规范化，实质是对系统行为施加约束，从而排除某些可能的事件时序。现有研究基于图解与代数形式的因果问题表达工具来探讨因果性，因果模型综合运用图解模型、结构方程、反事实逻辑与干预逻辑。图解模型作为表征人类对世界认知的语言，本文的研究方法聚焦于将因果图转换为TM模型，并对两类表示方法进行对比。结果表明，TM对因果关系的刻画更为完整，可为因果图提供理论基础。