In the field of explainable artificial intelligence (XAI), causal models and abstract argumentation frameworks are two formal approaches that provide a definition of an explanation. These symbolic approaches rely on logical formalisms to reason by abduction or to search for causalities, from the formal modeling of a problem or a situation. These models are designed to satisfy a number of properties found in explanations given by one human to another which are particularly interesting for humanmachine interactions as well. In this paper, we show the equivalence between a particular type of causal models, that we call argumentative causal graphs (ACG), and abstract argumentation frameworks. We also propose a transformation between these two systems and look at how one definition of an explanation in the argumentation theory is transposed when moving to ACG. To illustrate our proposition, we use a very simplified version of a screening agent for COVID-19.