Causal argumentation schemes to support sense-making in clinical genetics and law

With some sense-making software, investigators can use causal networks to visualize possible stories explaining the evidence. Despite the different domains, there are interesting correspondences between that type of application and a proposed intelligent learning environment (ILE) in which science students could visualize and debate causal scenarios accounting for clinical findings. The proposed ILE will extend the design of the GenIE Assistant, a system to generate first-draft genetic counseling letters. This paper compares the underlying computational models of sense-making software and the GenIE Assistant. Then it discusses refinements of the Assistant's causal argumentation schemes to support debate in the ILE. The refinements are at a level of abstraction that seem applicable to computational models for sense-making and evidential reasoning in law.