Boolean dimensions of causation; Regularity theory of causation; Philosophy of causation; Causal inference; Scientific methodology; Qualitative Comparative Analysis; Philosophy of biology; Philosophy of science; Philosophy of the social sciences; Causal data analysis; Coincidence Analysis
Baumgartner MIchael, Casini Lorenzo (2017), An Abductive Theory of Constitution, in Philosophy of Science
, 84, 214-233.
Thiem Alrik (2016), Analyzing Multilevel Data with QCA: Yet Another Straightforward Procedure, in Quality & Quantity: International Journal of Methodology
, 50(1), 121-128.
Thiem Alrik, Baumgartner Michael (2016), Back to Square One: A Reply to Munck, Paine and Schneider, in Comparative Political Studies
, 49, 801-806.
Thiem Alrik, Spöhel Reto, Dusa Adrian (2016), Enhancing Sensitivity Diagnostics for Qualitative Comparative Analysis: A Combinatorial Approach, in Political Analysis
, 24(1), 104-120.
Thiem Alrik, Baumgartner Michael (2016), Modeling Causal Irrelevance in Evaluations of Configurational Comparative Methods, in Sociological Methodology
, 46, 345-357.
Thiem Alrik, Baumgartner Michael (2016), Standards of Good Practice and the Methodology of Necessary Conditions in Qualitative Comparative Analysis, in Political Analysis
, 24, 478-484.
Thiem Alrik, Baumgartner Michael, Bol Damien (2016), Still Lost in Translation! A Correction of three Misunderstandings between Configurational Comparativists and Regressional Analysts, in Comparative Political Studies
, 49, 742-774.
Dusa Adrian, Thiem Alrik (2015), Enhancing the Minimization of Boolean and Multivalue Output Functions with eQMC, in Journal of Mathematical Sociology
, 39(2), 92-108.
Baumgartner Michael, Thiem Alrik (2015), Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis, in The R Journal
Baumgartner Michael, Thiem Alrik (2015), Model Ambiguities in Configurational Comparative Research, in Sociological Methods & Research
Thiem Alrik (2015), Parameters of Fit and Intermediate Solutions in Multi-Value Qualitative Comparative Analysis, in Quality & Quantity: International Journal of Methodology
, 49(2), 657-674.
Baumgartner Michael (2015), Parsimony and Causality, in Quality & Quantity
, 49, 839-856.
Thiem Alrik (2014), Navigating the Complexities of Qualitative Comparative Analysis: Case Numbers, Necessity Relations, and Model Ambiguities, in Evaluation Review
, 38, 487-513.
Thiem Alrik, Baumgartner Michael, Back to Square One: A Reply to Munck, Paine and Schneider, in Comparative Political Studies
Thiem Alrik, Conducting Configurational Comparative Research With Qualitative Comparative Analysis: A Hands-on Tutorial for Applied Evaluation Scholars and Practitioners, in American Journal of Evaluation
Baumgartner Michael, Wilutzky Wendy, Is It Possible to Experimentally Determine the Extension of Cognition?, in Philosophical Psychology
Thiem Alrik, Baumgartner Michael, Modeling Causal Irrelevance in Evaluations of Configurational Comparative Methods, in Sociological Methodology
Baumgartner Michael, Thiem Alrik, Often Trusted But Never (Properly) Tested: Evaluating Qualitative Comparative Analysis, in Sociological Methods & Research
Baumgartner Michael, The Inherent Empirical Underdetermination of Mental Causation, in Australasian Journal of Philosophy
This project aims to bring Coincidence Analysis (CNA) -- a Boolean method of causal data analysis -- from the methodological drawing board to effective, flexible, and computer-assisted applicability in real-life contexts of causal discovery. Moreover, Boolean causal modeling shall be supplied with a transparent and solid theoretical and conceptual foundation.In this vein, a precise understanding of the domain of applicability and the inferential potential of CNA shall be gained. Moreover, ways for integrating CNA and other methodological frameworks shall be explored. The output of the project will be a fully worked out, ready-to-use, maximally general, and theoretically grounded method of Boolean causal data analysis that is applicable to data not processable by other methods and that, thus, constitutes a valid alternative for researchers interested in Boolean dimensions of causality.