Heuristic greedy search algorithms for latent variable models
Peter Spirtes, Thomas Stuart Richardson, Chris E. Meek, Chris E. Meek · 1997
this paper we will describe how to extend search algorithms developed for non-latent variable DAG models to the case of DAG models with latent variables. We will introduce two generalizations of DAGs, called mixed ancestor graphs (or MAGs) and partial ancestor graphs (or PAGs), and briefly describe how they can be used to search for latent variable DAG models, to classify, and to predict the effects of interventions in causal systems.