Re-Establishing a Lost Connection: Multi-Value Logic in Causal Data Analysis in Social Science Disciplines

The main purpose of logic optimization lies in architecting integrated switching circuits. It is thus a topic well covered in electrical engineering. Some subfields of the social sciences have also employed algorithms for logic optimization since the mid-1980s to infer about cause-effect relations. Most notably, political scientists and sociologists have developed Qualitative Comparative Analysis (QCA), a configurational comparative method (CCM) that relies on the well-known Quine-McCluskey algorithm. However, while electrical engineering has progressed considerably since the advent of logic optimization in the 1950s, these advancements have not been monitored by social scientists. Nor have social scientists sought to establish interdisciplinary collaboration. The objective of our article is twofold. First, and more generally, we seek to build a bridge between electrical engineering and configurational causal inference. Secondly, and more specifically, we present Combinational Regularity Analysis (CORA), a new CCM that has been inspired by electrical engineering. In particular, we introduce one of CORA’s algorithms for optimizing highly unspecified multi-value logic functions with multiple outputs. The availability of such algorithms in CORA pushes the boundaries of configurational causal inference and attests to the extent to which configurational comparative methodology could benefit from more interdisciplinary collaboration with electrical engineering.