A unified approach to estimation and control of the False Discovery Rate in Bayesian network skeleton identification

Constraint-based Bayesian network (BN) structure learning algorithms typically control the False Positive Rate (FPR) of their skeleton identification phase. The False Discovery Rate (FDR), however, may be of greater interest and methods for its utilization by these algorithms have been recently devised. We present a unified approach to BN skeleton identification FDR estimation and control and experimentally evaluate the performance of FDR estimators in both tasks over several networks. We demonstrate that estimation is too conservative for most networks and strong control at common FDR thresholds is not achieved with some networks; finally, we identify the possible causes of this situation.