Root cause analysis of rare but catastrophic events in the chemical process industry must deal with the challenges of data scarcity that may lead to inaccurate diagnosis. Previously, Bayesian models (BMs) have been applied with fault trees to account for data scarcity. However, the BM does not account for source-to-source variability in collected data. To deal with this limitation, this work proposes a new framework to simultaneously handle data scarcity and source-to-source variability. For the purpose of computational efficiency, it first identifies key process variables (KPVs) for rare events using a sequential combination of relative information gain and Pearson correlation coefficient. Then, it performs the root cause analysis of KPV deviations using the Hierarchical Bayesian Model with an informative prior constructed from process data to handle source-to-source variability. Finally, performance of the proposed framework is demonstrated through a case study of the Tennessee Eastman process.