ASSOCIATE EDITORS: Naveen Donthu

In the social sciences, there is a great deal of talk about the importance of " theory " in constructing causal explanations of bodies of data.... In many of these cases the necessity of theory is badly exaggerated. It is a capital mistake to theorize before you have all the evidence. It biases the judgment. —Sherlock Holmes in Sir Arthur Conan Doyle's (1887) A Study in Scarlet. Users of structural equation modeling (SEM), a method that has also been known as causal modeling, have learned to tread lightly around the subject of causality. After all, the empirical raw material of SEM is typically a covariance matrix derived from nonexperimental data, and research dogma indicates that this is insufficient for making statements about cause-and-effect relations. At least as far back as Robert Ling's (1982) scathing review of David A. Kenny's (1979) book, Correlation and Causality (a classic text that is still well worth reading), users of SEM methods have found themselves on the defensive, careful not to claim too much. This, however, has produced something of a paradox. The models estimated with SEM clearly depict variable A as having an effect on variable B and distinguish between covariance relations and directional paths—that is, causal effects. Thus, SEM users propose structures that are causal but tend to disavow the causal element when they evaluate their results. Especially for the practitioner, the causal component is likely to be the point of the whole exercise: What a manager wants to know is, " If I do X, how will that change Y? " Furthermore, reasonable people use causal language and reach causal conclusions all the time. The government releases economic statistics, the stock market subsequently moves, and the observer concludes that the new information moved the market. Rain falls, water drips through a hole in the roof, and an observer makes the connection. People do this without the aid of either experiments or sophisticated data analysis. True, sometimes the observers are wrong—for example, at one point medical researchers thought exposure to aluminum was a cause of Alzheimer's syndrome, but now they consider the linkage spurious. Nevertheless, people proceed through life making causal inferences from nonex-perimental data. (2d ed.) urge researchers to resolve the paradox by dropping the pretense and acknowledging the causal content of their models. A → B means more than that A is correlated with B. It means that B changes in …