cna - Causal Modeling with Coincidence Analysis
Provides comprehensive functionalities for causal modeling
with Coincidence Analysis (CNA), which is a configurational
comparative method of causal data analysis that was first
introduced in Baumgartner (2009)
<doi:10.1177/0049124109339369>, and generalized in Baumgartner
& Ambuehl (2020) <doi:10.1017/psrm.2018.45>. CNA is designed to
recover INUS-causation from data, which is particularly
relevant for analyzing processes featuring conjunctural
causation (component causation) and equifinality (alternative
causation). CNA is currently the only method for INUS-discovery
that allows for multiple effects (outcomes/endogenous factors),
meaning it can analyze common-cause and causal chain
structures. Moreover, as of version 4.0, it is the only method
of its kind that provides measures for model evaluation and
selection that are custom-made for the problem of
INUS-discovery.
