Finding causal associations between two events or sets of events with relatively low frequency is very useful for various real-world applications. For example, a drug used at an appropriate dose may cause one or more adverse drug reactions (ADRs), although the probability is low. Discovering this kind of causal relationships can help us prevent or correct negative outcomes caused by its antecedents. However, mining these relationships is challenging due to the difficulty of capturing causality among events and the infrequent nature of the events of interest in these applications. In this paper, we try to employ a knowledge-based approach to capture the degree of causality of an event pair within each sequence since the determination of causality is often ultimately application or domain dependent. We then develop an interestingness measure that incorporates the causalities across all the sequences in a database. We developed and incorporated an exclusion mechanism that can effectively reduce the undesirable effects caused by frequent events. Our new measure is named exclusive causal-leverage measure. We proposed a data mining algorithm to mine ADR signal pairs from electronic patient database based on the new measure. The algorithm’s computational complexity is analyzed. We compared our new exclusive causal-leverage measure with our previously proposed causal-lever-age measure as well as two traditional measures in the literature: leverage and risk ratio. To establish the superiority of our new measure, we did extensive experiments. In our previous work, we tested the effectiveness of the causal-leverage measure using a single drug in the experiment. We propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient data sets where the drug-related events of interest occur infrequently. A data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs).