Configurational Causal Modeling and Logic Regression

Configurational comparative methods (CCMs) and logic regression methods (LRMs) are two families of exploratory methods that employ very different techniques to analyze data generated by causal structures featuring conjunctural causation and equifinality. 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 purpose of this article is to change that. We first level the field for readers from both backgrounds by providing brief introductions to the basic ideas behind CCMs and LRMs. Then, we carve out the strengths and weaknesses of the two method families by benchmarking their performance when applied to binary data under a variety of different discovery contexts. It turns out that CCMs and LRMs have complementary strengths and weaknesses. This creates various promising avenues for cross-validation.

Qualitative Comparative Analysis and robust sufficiency

Some methodologists take the search target of Qualitative Comparative Analysis (QCA) to be causal INUS-conditions, others contend that QCA should instead be used to search for some form of sufficiency that is more substantive than mere Boolean sufficiency. While the notion of an INUS-condition has a long and uncontroversial definitional history, Adrian Dusa, in a recent paper, is the first to explicitly define a notion of substantive sufficiency, which he labels robust sufficiency. Dusa’s definition, however, is vacuous in real-life research contexts. As an alternative, the first part of this paper non-vacuously defines robust sufficiency and supplies a corresponding notion of minimality, which—I argue—captures Dusa’s conceptual intentions. In the second part, I then report and discuss the results of a series of simulation experiments benchmarking the performance of the different QCA solution types in recovering robust sufficiency and minimality.