Research Projects as PI
Current and past research projects on coincidence analysis, causal modeling, constitution, and interventionism.
Allocated funds. NOK 12M.
Background. Coincidence Analysis (CNA) is a configurational comparative method of causal data analysis that was first introduced in (Baumgartner 2009a, 2009b), substantively re-worked and generalized in (Baumgartner and Ambühl 2020), and implemented in a software library of the R environment for statistical computing in (Ambühl and Baumgartner 2020). In recent years, CNA was applied in numerous studies in public health as well as in the social and political sciences. For example, Dy et al. (2020) used CNA to investigate how different implementation strategies influence patient safety culture in medical homes. Yakovchenko et al. (2020) applied the method to data on the factors affecting the uptake of innovation in the treatment of hepatitis C virus infection, while Haesebrouck (2019) drew on CNA to search for factors influencing EU member states’ participation in the military operations in Libya and against the Islamic State. In contrast to more standard methods of data analysis, which primarily quantify effect sizes, CNA belongs to a family of methods designed to group causal influence factors conjunctively (i.e. in complex bundles) and disjunctively (i.e. on alternative pathways). It is firmly rooted in a so-called regularity theory of causation 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.
Main goals. The development of CNA is not finished. The AdCNA project will address four remaining weaknesses and limitations of the method. First, CNA’s applicability, which is currently limited to data on a maximum of about 15 factors, shall be extended to data of significantly higher dimensionality. Second, we will develop CNA-specific inference tests to further improve the quality of the method’s output-at present, that quality is not high enough when the data have small sample sizes and high noise levels. Third, new measures of fit and solution attributes will be devised for model selection. Fourth, by applying CNA in studies on auditory hallucinations and infant mortality, we will extend the scope of CNA applications to psychology and epidemiology. Overall, CNA has proven its value in some disciplines. But to establish itself in the methodological toolbox of the special sciences, more algorithmic power and flexibility, more output reliability, and wider dissemination are needed. The AdCNA project sets out to deliver exactly that.
Collaborators on this project:
The Peder Sather Center for Advanced Study supports projects carried out by researchers at UC Berkeley in collaboration with researchers from eight Norwegian universities. This project brings together Prof. Emmeline Chuang (UC Berkeley) and Prof. Michael Baumgartner (University of Bergen) to organize a CNA training at UC Berkeley in spring 2026 and to facilitate virtual collaboration between both teams.
The comparative study focuses on an applied health services research question: why the U.S. spends more per capita on health care than other high-income countries while at the same time showing worse health outcomes, including higher rates of multiple chronic conditions and higher death rates for treatable conditions. The project compares results generated with CNA to those produced by quantitative regression-based approaches.
Allocated funds. NOK 1.176M.
The Akademieavtalen is part of the long-term research collaboration between the University of Bergen and Equinor. Its purpose is to support basic research and research-based education in strategically important areas covered by the agreement.
This project investigates points of contact between Coincidence Analysis (CNA), machine learning, and Bayesian network methods, with a particular focus on how these approaches may inform one another in the analysis of complex data.
In the context of this project, Jonas Wahl was appointed as Professor II.
Allocated funds. NOK 17M.
Since the late 1980ies, configurational comparative methods (CCMs) have gradually been added to the methodological toolkit in disciplines as diverse as political science, sociology, business administration, management, environmental science, evaluation science, and public health. The most prominent CCM is Qualitative Comparative Analysis (QCA) (Ragin 2008). QCA, however, is unsuited to analyze causal structures with more than one endogenous variable, e.g. structures with common causes or causal chains. To overcome that restriction, Coincidence Analysis (CNA) has been first introduced in Baumgartner (2009a, 2009b). It has meanwhile been generalized in Baumgartner & Ambuehl (2020) and is available as software package for the R environment (Ambuehl & Baumgartner 2020).
This project has three objectives. The first is to fill all remaining gaps in the methodological protocol of CNA and to complement the CNA R-package accordingly. In particular, tools for robustness tests of CNA models shall be developed. The second objective is to systematically test the inferential potential of CNA by applying it to real-life studies from varying disciplines and, thereby, to explore the applicability of CNA outside of the standard domain of CCMs. The third objective is to analyze the relationship between CNA and methods from other theoretical traditions-in particular Bayes-nets methods (cf. Spirtes et al. 2000; Pearl 2000) and regression-analytical methods (Gelman and Hill 2007). Are there substantive points of contact between these methodological traditions? Are there ways to fruitfully integrate them in multi-method studies? What are the conditions that determine what method is best suited to investigate a given phenomenon or to answer a given research question?
Collaborators on this project:
Allocated funds. CHF 1.355M.
Background. Coincidence Analysis (CNA) was first developed as a Boolean method for causal data analysis on the methodological drawing-board and against the background of a range of idealizing assumptions. In that early form, its applicability to real-life data was still restricted, it could only model dichotomous variables, and no operative software implementation yet existed. At the same time, CNA already showed important advantages over the then dominant Boolean approach, Qualitative Comparative Analysis (QCA), in particular with respect to causal chains, redundancy elimination, and the analysis of structures without an antecedent partition into exogenous and endogenous variables.
Main goals. This project aimed to bring Coincidence Analysis from the drawing-board to effective, flexible, and computer-assisted applicability in real-life contexts of causal discovery. In collaboration with researchers working with Boolean causal models, CNA was to be adapted to the demands of its users, generalized for continuous variables, and extensively tested on real-life data from different disciplines. The overall objective was to develop a fully worked out and ready-to-use method of Boolean causal data analysis with a clearly defined domain of applicability and inferential potential.
Collaborators on this project: