Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis

Abstract We present cna , a package for performing Coincidence Analysis (CNA). CNA is a config-urational 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 themethod’s theoretical background and main algorithmic ideas, we demonstrate the use of the packageby means of an artificial and a real-life data set. Moreover, we outline planned enhancements of thepackage that will further increase its applicability. Introduction Configurational comparative methods (CCMs) subsume techniques for the identification of complexcausal dependencies in configurational data using the theoretical framework of Boolean algebra andits various extensions (Rihoux and Ragin,2009). For example, Qualitative Comparative Analysis(QCA;Ragin,1987,2000,2008)—hitherto the most prominent representative of CCMs—has beenapplied in areas as diverse as business administration (e.g.,Chung,2001), environmental science(van Vliet et al.,2013), evaluation (Cragun et al.,2014), political science (Thiem,2011), public health(Longest and Thoits,2012) and sociology (Crowley,2013). Besides three stand-alone programs basedon graphical user interfaces, three R packages for QCA are currently available, each with a differentscope of functionality: