Evaluating the creation and interpretation of causal influence models

Bayesian networks (BNs) are probabilistic models frequently used to capture domain knowledge for use in computational systems that can reason about states, causes, and effects. While BNs have many advantages, their complexity can hamper the process of knowledge elicitation and encoding. First, domain experts may not have expertise in artificial reasoning or probabilistic models, and that lack of understanding may complicate the elicitation of probabilities relevant to BN model structure. In addition, BNs require the definition of a priori, conditional probabilities: for complex models, this requires eliciting large numbers of complex probabilities. Multiple "canonical modeling" approaches, such as Causal Influence Models (CIMs), have been developed to address these complexities. However, little progress has been made towards human-in-the-loop evaluation of such approaches - specifically, their accessibility and usability, their related user interfaces, and how they enable a user to correctly create and interpret variables and probabilistic relationships. In this study, we evaluated the CIM approach (implemented in a software application) to determine the effect on user task performance. Results indicate that the model complexity has an adverse effect on performance when users are interpreting an existing model; that semantics of a model may impact performance; and that users were generally successful in creating new models of different situations.