Enumerating unlabeled and root labeled trees for causal model acquisition

Abstract

To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs to be assessed for each node. It generally has the complexity exponential on n. The non-impeding noisy-AND (NIN-AND) tree is a recently developed causal model that reduces the complexity to linear, while modeling both reinforcing and undermining interactions among causes. Acquisition of an NIN-AND tree model involves elicitation of a linear number of probability parameters and a tree structure. Instead of asking the human expert to describe the structure from scratch, in this work, we develop a two-step menu selection technique that aids structure acquisition. © 2009 Springer Berlin Heidelberg.