Coincidence Analysis (CNA)

Coincidence Analysis (CNA) is a configurational comparative method (CCM) of causal inference and data analysis that is custom-built for multi-outcome structures.

This page begins to collect relevant literature on CNA and will inform about new developments, seminars, workshops etc.

Training

upcoming

Indianapolis (via Zoom), Aug. 3-5, 2021, Introduction to Causal Modeling with Coincidence Analysis

Indianapolis (via Zoom), 2020, Advanced Topics in Configurational Causal Data Analysis
Indianapolis (via Zoom), 2020, Beginners’ Introduction to Configurational Causal Data Analysis
Arlington, 2019: Coincidence Analysis (CNA) Workshop at the D&I Conference
Indianapolis, 2017: Configurational Research with QCA and CNA
Konstanz, 2016: Configurational Research with QCA and CNA
Geneva, 2015: Configurational Research with QCA and CNA

Software

Base package cna

The cna package provides all the functionalities required to analyze data by means of CNA.

On 05/28/2021, version 3.1.0 of the cna R-package has been released on CRAN.
On 11/06/2020, update 3.0.1 has been released on CRAN.
On 09/13/2020, version 3.0.0 of the cna R-package has been released on CRAN.
On 05/20/2020, update 2.2.3 has been released on CRAN.
On 06/12/2019, update 2.2.2 has been released on CRAN.
On 08/09/2019, update 2.2.1 has been released on CRAN.
On 04/13/2019, update 2.2.0 has been released on CRAN.
On 06/13/2018, update 2.1.1 has been released on CRAN.
On 06/01/2018, version 2.1.0 of the cna R-package has been released on CRAN.

Add-on package cnaOpt

The cnaOpt package provides various functions for optimizing consistency and coverage scores of models of configurational comparative methods as CNA and QCA.

On 09/14/2020, update 0.2.0 has been released on CRAN.
On 09/12/2019, update 0.1.1 has been released on CRAN.
On 21/10/2019, version 0.1.0 of the cnaOpt R-package has been released on CRAN.

Literature

Methodological

The details of the most recent version of CNA, which is implemented in the cna package, are described here:

M. Baumgartner and M. Ambühl (2020), cna: An R Package for Configurational Causal Inference and Modeling. R package vignette: The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/cna/vignettes/cna_vignette.pdf.
M. Baumgartner and M. Ambühl (2018), Causal Modeling with Multi-Value and Fuzzy-Set Coincidence Analysis, Political Science Research and Methods. Replication material at https://doi.org/10.7910/DVN/YIEAF1; [penultimate draft]

This paper provides a theoretical introduction the functions of the cnaOpt package:

M. Baumgartner and M. Ambühl (2021), Optimizing Consistency and Coverage in Configurational Causal Modeling, Sociological Methods & Research. [penultimate draft]; Replication material.

This paper introduces a robustness measure for CNA:

V.P. Parkkinen and M. Baumgartner (2021), Robustness and Model Selection in Configurational Causal Modeling, Sociological Methods & Research. [penultimate draft]

Older CNA versions are introduced/discussed here:

M. Baumgartner and A. Thiem (2015), Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis, The R Journal 7, 176-184. [penultimate draft]
M. Baumgartner (2013), Detecting Causal Chains in Small-N Data, Field Methods 25, 3-24. [penultimate draft]
M. Baumgartner (2009), Uncovering Deterministic Causal Structures: A Boolean Approach, Synthese 170, 71-96. [Springer Nature SharedIt] [penultimate draft]
M. Baumgartner (2009), Inferring Causal Complexity, Sociological Methods & Research 38, 71-101. [penultimate draft]

Applied

Deborah J. Cohen, Shannon M. Sweeney, William L. Miller, et al., Miguel Marino (2021), Improving smoking and blood pressure outcomes: The interplay between operational changes and local context, The Annals of Family Medicine, May 2021, 19 (3) 240-248; doi: 10.1370/afm.2668
J. Coury, E. Miech, P. Styer et al. (2021), What’s the “secret sauce?” How implementation variation affects the success of colorectal cancer screening outreach. Implement Sci Commun 2, 5 https://doi.org/10.1186/s43058-020-00104-7
R.G. Whitaker, N. Sperber, M. Baumgartner, et al. (2020), Coincidence analysis: a new method for causal inference in implementation science, Implementation Science 15, 108 (2020). https://doi.org/10.1186/s13012-020-01070-3
Petrik, A., B. Green, J. Schneider et al. (2020), Factors influencing implementation of a colorectal cancer screening improvement program in community health centers: an applied use of configurational comparative methods, Journal of General Internal Medicine, doi: 10.1007/s11606-020- 06186-2.
Sydney M. Dy et al. (2020), Association of Implementation and Social Network Factors With Patient Safety Culture in Medical Homes. A Coincidence Analysis, The Journal of Patient Safety, doi: 10.1097/PTS.0000000000000752
S. E. Hickman, E. Miech et al. (2020), Identifying the Implementation Conditions Associated With Positive Outcomes in a Successful Nursing Facility Demonstration Project , The Gerontologist, doi: 10.1093/geront/gnaa041
V. Yakovchenko, E. Miech et al. (2020), Strategy Configurations Directly Linked to Higher Hepatitis C Virus Treatment Starts. An Applied Use of Configurational Comparative Methods, Medical Care 58(5), p. e31-e38, doi: 10.1097/MLR.0000000000001319
W. Moret, and L. Lorenzetti (2020), Realistic expectations: exploring the sustainability of graduation outcomes in a program for children affected by HIV in Kenya’s Northern Arid Lands. Vulnerable Children and Youth Studies. doi: 10.1080/17450128.2020.1738022
T. Haesebrouck (2019), Who follows whom? A coincidence analysis of military action, public opinion and threats, Journal of Peace Research, doi: 10.1177/0022343319854787
R. Epple and S. Schief (2016), Fighting (for) gender equality: the roles of social movements and power resources, Interface: a journal for and about social movements, Vol. 8, No.2., 394- 432.
M. Baumgartner and R. Epple (2014), A Coincidence Analysis of a Causal Chain: The Swiss Minaret Vote, Sociological Methods & Research 43, 280-312. [penultimate draft]