#3207. Configurational Causal Modeling and Logic Regression

October 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Statistics and Probability;
Arts and Humanities (miscellaneous);
Experimental and Cognitive Psychology;
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Abstract:
Configurational comparative methods (CCMs) and logic regression methods (LRMs) are two exploratory methods that help analyze data generated by causal structures. Aiming for the same by different means carries a substantive synergy potential, which, however, remains untapped so far because representatives of the two frameworks know little of each other. The scholars first level the field for readers from both backgrounds by providing brief introductions to the basic ideas behind CCMs and LRMs. Then, the research carves out the strengths and weaknesses of the two methods by benchmarking their performance. It turns out that CCMs and LRMs have complementary strengths and weaknesses. This creates various promising avenues for cross-validation.
Keywords:
Coincidence Analysis; component causation; conjunctural causation; cross-validation; equifinality; INUS causation; multi-method research

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