1. A computer implemented method for evaluating performances of generative models, comprising: receiving observations associated with observable variables of a first generative model and a second generative model, each of the first and second generative models having information about causal relationships among hidden and observable variables; using at least one computer, for each observation, applying the first model to the observation to produce a first assessment of the observation, applying the second model to the observation to produce a second assessment of the observation, and determining a similarity score between the first and second assessments; and outputting a subset of the observations, each corresponding to first and second assessments, having a similarity score within a predetermined range.
1. A computer implemented method for evaluating performances of generative models, comprising: receiving observations associated with observable variables of a first generative model and a second generative model, each of the first and second generative models having information about causal relationships among hidden and observable variables; using at least one computer, for each observation, applying the first model to the observation to produce a first assessment of the observation, applying the second model to the observation to produce a second assessment of the observation, and determining a similarity score between the first and second assessments; and outputting a subset of the observations, each corresponding to first and second assessments, having a similarity score within a predetermined range. 4. The method of claim 1 in which the first generative model is trained using a first set of training documents, the second generative model is trained using a second set of training documents, and the second generative model is an updated version of the first generative model.