  Causal models are playing an increasingly important role in machine learning,
particularly in the realm of explainable AI. We introduce a conceptualisation
for generating argumentation frameworks (AFs) from causal models for the
purpose of forging explanations for the models' outputs. The conceptualisation
is based on reinterpreting desirable properties of semantics of AFs as
explanation moulds, which are means for characterising the relations in the
causal model argumentatively. We demonstrate our methodology by reinterpreting
the property of bi-variate reinforcement as an explanation mould to forge
bipolar AFs as explanations for the outputs of causal models. We perform a
theoretical evaluation of these argumentative explanations, examining whether
they satisfy a range of desirable explanatory and argumentative properties.
