Integrating Locally Learned Causal Structures with Overlapping Variables

In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset. While there are asymptotically correct, informative algorithms for discovering causal relationships from a single dataset, even with missing values and hidden variables, there have been no such reliable procedures for distributed data with overlapping variables. We present a novel, asymptotically correct procedure that discovers a minimal equivalence class of causal DAG structures using local independence information from distributed 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.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Robert E. Tillman, David Danks, Clark Glymour
• Keywords: Dag Structure, Information Technology, Missing Values, NIPS 2008, Single Datasets