  Research in cognitive psychology has established that whether people prefer
simpler explanations to complex ones is context dependent, but the question of
`simple vs. complex' becomes critical when an artificial agent seeks to explain
its decisions or predictions to humans. We present a model for abstracting
causal reasoning chains for the purpose of explanation. This model uses a set
of rules to progressively abstract different types of causal information in
causal proof traces. We perform online studies using 123 Amazon MTurk
participants and with five industry experts over two domains: maritime patrol
and weather prediction. We found participants' satisfaction with generated
explanations was based on the consistency of relationships among the causes
(coherence) that explain an event; and that the important question is not
whether people prefer simple or complex explanations, but what types of causal
information are relevant to individuals in specific contexts.
