Coincidence Analysis (CNA) is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009a, 2009b). It is related to the more well-known method of Qualitative Comparative Analysis (QCA) (Ragin 2008), but contrary to QCA, CNA is custom-built for the analysis of causal structures with multiple outcomes. So far, however, CNA has only been capable of processing dichotomous variables, which greatly limited its applicability to real-life data. This Paper generalizes CNA for multi-value and continuous variables whose values are interpreted as membership scores in fuzzy sets. This generalization comes with a major adaptation of CNA’s algorithmic protocol for building causal models, which turns out to give CNA an edge over QCA not only with respect to multi-outcome structures but also with respect to the analysis of noisy data stemming from single-outcome structures. Furthermore, the paper introduces the R-package cna that makes the whole inferential power of multi-value and fuzzy-set CNA available to end-users.
