Causal Inference in Statistical Models of the Process of Socioeconomic Achievement

Abstract

Typically, using data from a nonexperimental study, quantitative sociologists model one or more outcomes as a function of independent variables, interpreting the resulting parameter estimates as effects. This article compares the usual approach to causal inference in sociology with an alternative approach that builds explicitly on a counterfactual account of causation. The comparison is relevant because sociologists typically interpret (incorrectly) parameter estimates obtained using the first approach as supporting causal statements that are counterfactual. To make matters concrete, the author reconsiders an attainment model of Featherman and Hauser, who are interested in comparing the effects of family background on achievement, by sex and time. This analysis suggests the coefficients in the regressions of respondent's education and occupational status on background should not be interpreted as effects. However, because a child's sex is determined without regard for his or her subsequent achievements, sex may be viewed as randomly assigned, justifying treating sex as the cause and the background variables as covariates. Unfortunately, the way in which the data were collected preclude such a treatment.