Hierarchial causal models are described as pictorial representations of multiple regression equations. These models are particularly helpful for three reasons: (1) the formulation of problems in a path analytic framework forces a degree of explicitness that is often not present in research reports that rely solely on regression; (2) they provide a powerful aid to the substantive interpretation of results; and (3) they aid in the interpretation of relationships between unmeasured variables. Though causal modeling techniques are very powerful, important prerequisites are a thorough knowledge of one's subject matter and a stylish appreciation of alternative explanations. (BW)