Abstract: Systems and methods are described for automatically identifying cause-effect relationships using hybrid systems. A server may retrieve selected expert rule templates that have input parameters that match the parameters of event data objects derived from a stream of input data. Cause-effect relationships may be determined between parameters of the data set when the selected expert rule templates are satisfied. The rule-based aspect is augmented using statistical correlation to identify a correlated pair of different parameters based on pairwise comparison of all parameters of a data set and a coincidence probability of the different parameters. Using the identified correlated pair, the server may create a new expert rule template in an expert rule database. Subsequent data in the stream of input data may trigger generating an alert when one of the expert rule templates is contradicted by the incoming data, thereby ensuring that the expert rule templates are up-to-date and accurate.