Coincidence Analysis

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Coincidence Analysis (CNA) is a configurational comparative method of causal inference and data analysis grouping causes into bundles that are jointly effective and placing them on alternative causal routes to their effects. The method is custom-built for uncovering multi-outcome structures, even when they produce no or only weak pairwise dependencies between endogenous and exogenous factors. This page collects relevant information on CNA and on the corresponding research projects at the University of Bergen.

Coincidence Analysis (CNA) is a configurational comparative method of causal data analysis that was first introduced in (Baumgartner 2009a2009b), substantively re-worked and generalized in (Baumgartner and Ambühl 2020), and implemented in the software libraries cna, frscore, and cnaOpt for the R environment for statistical computing. In recent years, CNA was applied in numerous studies in public health as well as in the social, political, and behavioral sciences (e.g. Dy et al. 2022,  Knott et al. 2022, Miech et al. 2021, Swiatczak 2021, Whitaker et al. 2020, Yakovchenko et al. 2020, Haesebrouck 2019; more applications are collected here). 

While most standard methods of causal data analysis require that causation manifests as some non-zero pairwise dependence between causes and effects in the data, CNA belongs to a family of methods--comprising Qualitative Comparative Analysis (QCA; e.g. Ragin 2008) or Logic Regression (LR; e.g. Ruczinski et al. 2003), among others--that are capable of analyzing structures in which causes and effects are pairwise independent. While standard methods primarily quantify effect sizes, CNA groups causal influence factors conjunctively (i.e. in complex bundles) and disjunctively (i.e. on alternative pathways). CNA is firmly rooted in the so-called INUS theory of causation (see Mackie 1974, Baumgartner and Falk 2019) and it is the only method of its kind that can process data generated by causal structures with multiple outcomes (effects), for example, causal chains.

CNA is currently being developed further in two prominent research projects at the University of Bergen. The first is co-funded by the University of Bergen and the Trond Mohn Foundation in the context of the Toppforsk-programme; the second is funded by the Research Council of Norway in the context of the FRIPRO scheme.

The 2024 CNA training will be held from May 20-23 at RUSH University in Chicago. Registration is now open at https://rushu.rush.edu/cna2024.

The 3rd international conference on "Current Issues in Coincidence Analysis" will also be hosted by RUSH, May 24-25, 2024. See this site for details and registration.


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May 24

3rd International Conference on Current Issues in Coincidence Analysis

The third international conference dedicated exclusively to the configurational comparative method of Coincidence Analysis (CNA) will take place from May 24 to 25, 2024, at RUSH University in Chicago.

May 20

Introduction to Causal Data Analysis & Modeling with Coincidence Analysis

Learning causal data analysis with Coincidence Analysis from the ground up, with practical application to health equity and health services.

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New paper on data imbalances in CNA

Martyna Swiatczak and Michael Baumgartner investigate the conditions under which data imbalances are problematic for the performance of Coincidence Analysis (CNA).

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New R package for drawing causal Hypergraphs

Christoph Falk, Mathias Ambühl, and Michael Baumgartner released a new R package called causalHyperGraph on CRAN. It draws causal Hypergraphs from solution formulas of the CNA method.


New measures for evaluating CNA models

Luna De Souter identifies shortcomings of the two main measures for evaluating CNA models, consistency and coverage, and introduces two new evaluation measures.

Save the date: The next CNA training intensive will be held from May 20-23, 2024, at RUSH University in Chicago.