  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.
