Latent Variable Discovery Using Dependency Patterns
This addresses the challenge of identifying unmeasured variables in causal models, which is important for researchers in causal inference and machine learning, though it appears incremental as it builds on existing latent variable discovery concepts.
The paper tackles the problem of discovering latent variables in Bayesian networks by systematically searching for dependency patterns that cannot be explained without latent variables, enabling more rigorous latent variable discovery.
The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. However, some of those dependencies are generated by causal structures involving variables which have not been measured, i.e., latent variables. Some such patterns of dependency "reveal" themselves, in that no model based solely upon the observed variables can explain them as well as a model using a latent variable. That is what latent variable discovery is based upon. Here we did a search for finding them systematically, so that they may be applied in latent variable discovery in a more rigorous fashion.