Konolige, K. (1992). Abduction versus closure in causal theories. Artificial Intelligence, 53(2-3), 255-272.
There are two distinct formalizations for reasoning from observations to explanations, as in diagnostic tasks. The consistency based approach treats the task as a deductive one, in which the explanation is deduced from a background theory and a minimal set of abnormalities. In the other treatment, based on abduction, the explanations are considered to be sentences that, when added to the background theory, account for the observations. We show that there is a close connection between these two formalizations. Starting with a causal theory, explanations can be generated either by abductive reasoning, or by adding closure axioms and minimizing causation within a deductive framework. The latter method is strictly stronger than the former, but requires full knowledge of causation in a domain.