There have been many different approaches to investigate the causal relation-ships among variables in business and economics. Some of them emphasized the prior information for building a causal model (SEM). Others emphasized the posterior information from sample data for building a causal model (VARMA or TRA). There have been few studies to integrate the prior information and the posterior information in systematic way. Nevertheless, there were attempts to emphasize a restrictive role of prior information for building a causal model (SEMTSA). For the systematic integration of the two informations, a different view of system structure may be emphasized (FVAR). In this study the four approaches for tests of causality were developed. They are based on the three types of model from the two view of system structure: direct VARMA, pre-filtering, modified SEMTRA, and fuzzy VAR approaches. The two view of system structure are abstract set view and fuzzy set view. The three types of model are VARMA model, SEMTSA model, and FUZZY model. The practical procedures of the approaches were proposed for their implementations with programs. According to the test procedures the study used the term "top-down" for VARMA model, the term "bottom-up-top-down" for SEMTSA model, and the term "top-down-bottom-up" for FUZZY model. The study emphasized that it is up to one``s subjective consideration which of the three models will be adequately selected for a particular application area. In this study the three models were all implemented in an individual application. The study applied the three types of model to money are income data (quarterly) in Canada and stock and money data (monthly) in the United States. Their results of the applications are compared and interpreted according to the new approaches. The study concludes that the three approaches from the abstract set view does not frequently produce the inconsistent result for causation in the historical laboratory generating the bus...