Bayesian networks (BNs) are excellent tools for reasoning about uncertainty and capturing detailed domain knowledge. However, the complexity of BN structures can pose a challenge to domain experts without a background in artificial intelligence or probability when they construct or analyze BN models. Several canonical models have been developed to reduce the complexity of BN structures, but there is little research on the accessibility and usability of these canonical models, their associated

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... er interfaces, and the contents of the models, including their probabilistic relationships. In this paper, we present an experimental procedure to evaluate our novel Causal Influence Model structure by measuring users' ability to construct new models from scratch, and their ability to comprehend previously constructed models. [Results of our experiment will be presented at the workshop.]