Integrating Locally Learned Causal Structures with Overlapping Variables

In many domains, data are distributed among datasets that share only some variables; otherrecorded variables may occur in only one dataset. While there are asymptotically correct, informative algorithms for discovering causal relationships froma single dataset, even with missing values and hidden variables, there have been no such reliable procedures for distributed data with overlapping variables. Wepresent a novel, asymptotically correct procedure that discovers a minimal equivalence class of causal DAG structures using local independence information fromdistributed data of this form and evaluate its performance using synthetic and real-world data against causal discovery algorithms for single datasets and applying Structural EM, a heuristic DAG structure learning procedure for data with missing values, to the concatenated data.