Most of these are used to discover frequent event sequences from time-point-based events. However, these frequent event sequences do not clearly represent the causal relationship among events. If a sequence of events of the same type is summarized into one interval-based event, we can discover temporal interval relations from interval-based events. From these temporal interval relations, causal relations among interval-based events can be easily found. In this paper, we proposed a new method that mines causal relations from time-point-based events. Experiments were performed in this study to evaluate three methods (DFCR-FIR, DFCR-FIRc, and DFCR-CRc) to discover the causal relations and temporal interval relations from time-point-based events. DFCR-FIR and DFCR-FIRc discover frequent causal relations from frequent temporal interval relations. DFCR-CRc discovers frequent causal relations from the causal relations. We found that DFCR-CRc provides more useful and effective knowledge than two methods DFCR-FIR and DFCR-FIRc.