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 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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For attribution, please cite this work as
Baumgartner & Thiem, "Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis", The R Journal, 2015
BibTeX citation
@article{RJ-2015-014,
author = {Baumgartner, Michael and Thiem, Alrik},
title = {Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2015-014},
doi = {10.32614/RJ-2015-014},
volume = {7},
issue = {1},
issn = {2073-4859},
pages = {176-184}
}