Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis

We present cna, a package for performing Coincidence Analysis (CNA). CNA is a configurational comparative method for the identification of complex causal dependencies-in particular, causal chains and common cause structures-in configurational data. After a brief introduction to the method's theoretical background and main algorithmic ideas, we demonstrate the use of the package by means of an artificial and a real-life data set. Moreover, we outline planned enhancements of the package that will further increase its applicability.