Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis
نویسنده
چکیده
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.
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Causal Modeling with Multi-Value and Fuzzy-Set Coincidence Analysis
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 analyzing causal structures with multiple outcomes. So far, however, CNA has only been capable of proces...
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