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FRIHUMSAM-Fri prosj.st. hum og sam

Advancing Causal Modeling with Coincidence Analysis

Alternative title: Fremme årsaksmodellering med Koinzidensanalyse

Awarded: NOK 12.0 mill.

Project Number:

326215

Application Type:

Project Period:

2021 - 2025

Location:

Subject Fields:

Many causal structures have one or both of the following features: (i) causes are arranged in complex bundles that only become operative when all of their components are properly co-instantiated, each of which in isolation is ineffective or leads to different outcomes, and (ii) outcomes can be brought about along alternative causal routes such that, when one route is suppressed, the outcome may still be produced via another one. Traditional methods of causal data analysis face various problems when analyzing structures with these features. Coincidence Analysis (CNA) is a method custom-built to analyze data generated by causal structures featuring (i) and (ii). CNA is currently seeing a growing dissemination in public health as well as in the social and political sciences. But 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, we shall devise instruments for reducing model ambiguities, which are particularly severe when the data are heavily fragmented. Fourth, by applying CNA in studies on auditory hallucinations and infant mortality, we—in collaboration with partners—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.

Coincidence Analysis (CNA) is a method of causal data analysis first introduced in 2009, substantively generalized since then, and now available as an open source software package. In recent years, CNA was applied in numerous studies in public health and in the social and political sciences. It was, for example, used to investigate how different implementation strategies influence patient safety culture in medical homes, or what factors influence the uptake of innovation in hepatitis C virus treatment, or to search for the causes of EU member states’ participation in the military operations in Libya. 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). 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, we shall devise instruments for reducing model ambiguities, which are particularly severe when the data are heavily fragmented. Fourth, by applying CNA in studies on auditory hallucinations and infant mortality, we—in collaboration with partners—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.

Funding scheme:

FRIHUMSAM-Fri prosj.st. hum og sam