A Note of Caution in Causal Modelling

Many empirical investigations in the behavioral sciences today aim at tracing the causes of variations in some key dependent variable. The search for satisfying causal explanations is difficult because of the complexity of social phenomena, the crudeness of the measures of many important variables, and the prevalence of simultaneous cause and effect relations among variables. Although these difficulties remain, a number of important methodological contributions have clarified the conditions under which causal inferences can be made from non-experimental data. In particular the Simon-Blalock technique has recently gained considerable attention, and has been profitably used by a number of political scientists in their research. Examination of some of these applications does, however, reveal the need for a better understanding of the purposes and limitations of the technique. This paper reviews two studies: (1) the re-analysis of the Miller-Stokes data by Cnudde and McCrone, and (2) the analysis of the determinants of Negro political participation in the South by Matthews and Prothro. We shall argue that both these applications have two faults: (1) a failure to distinguish conclusions from assumptions, and (2) an inadequate correspondence between the assumptions made in constructing the mathematical models and our prior knowledge about the phenomena being studied. In addition, we shall use the first study to illustrate a principle of general importance in causal analysis: the investigator should check the possibility that different causal mechanisms occur in different subgroups of his data. And we shall use the second study to illustrate the difficulty of separating the effects of two highly correlated independent variables.