A growing body of literature suggests that Qualitative Comparative Analysis (QCA) can be used either in support or in lieu of mainstream statistical and econometric techniques.  Some of these articles focus specifically on the use of QCA for policy analysis.  A book devoted entirely to applications for policy analysis appeared in 2010 and the journal Policy & Society recently devoted an entire issue to championing QCA as a tool for policy research.  This paper argues that the claims in this body of work rest on an erroneous understanding of policy analysis and of the power of traditional research methods to answer important policy questions.  This argument is supported by an analysis of recent articles in the Journal of Policy Analysis and Management (JPAM), examples of policy analysis based on QCA, and a simulation-based reanalysis of the Cal Learn policy experiment using QCA and regression.

The results of this analysis suggest that QCA adds little value to current methods of policy scholarship.  The sample of articles reveals that QCA proponents are mistaken about the neglect of causal complexity in current, rigorous policy research.  The simulation results show that QCA is unable to uncover a complex treatment effect that is readily revealed with standard econometric methods.  Moreover, conceptualizing policy outcomes in terms of bounded sets and then scoring cases according to membership in those sets forces causal effects into a set-theoretic framework ill equipped to uncover meaningful variation in outcomes.