Root cause analysis includes the methods to identify the sources of errors in a network. Most techniques rely on knowledge models of the system, which are usually built by using network operators' expertise. This presents problems related to knowledge extraction, scalability, and understandability. We propose an offline method based on machine learning techniques for the automatic identification of dependencies between system events, enhanced with summarization, operations on graphs, and visualization that help network operators identify the root causes of errors. We illustrate it with examples from a corporate network.