The listening pattern prescribed by the paths of the causal model usually results in observable patterns or dependencies in the data. These patterns are called “testable implications” because they can be used for testing the model. These are statements like “There is no path connecting D and L,” which translates to a statistical statement, “D and L are independent,” that is, finding D does not change the likelihood of L. If the data contradict this implication, then we need to revise our model. Such revisions require another engine, which obtains its inputs from boxes 4 and 7 and computes the
...more
