Weaknesses of DAGs for imprecise general causal representations

Causal reasoning occupies a central position in human reasoning. In order to algorithmically consider causal relations, the relations must be placed into a representation that supports manipulation. The most widespread causal representation is directed acyclic graphs (DAGs). However, DAGs are severely limited in what portion of the every day world they can represent. Both possible causal relationships and shifts in grain size are overly limited. Commonsense reasoning recognizes causal granularization. Sometimes, the details underlying an event can be known to a fine level of detail, sometimes not; causal representations must accommodate shifts in grain size. Every day reasoning approaches are used that do not require complete knowledge. An algorithmic way of handling and representing causal imprecision is needed.