Parsimony and Causality

This paper takes issue with the current tendency in the literature on Qualitative Comparative Analysis (QCA) to settle for so-called intermediate solution formulas, in which parsimony is not maximized. I showthat there is a tight conceptual connection between parsimony and causality: onlymaximally parsimonious solution formulas reflect causal structures. However, in order to maximize parsimony, QCA-due to its reliance on Quine-McCluskey optimization (Q-M)-is often forced to introduce untenable simplifying assumptions. The paper ends by demonstrating that there is an alternative Boolean method for causal data analysis, viz. Coincidence Analysis (CNA), that replaces Q-M by a different optimization algorithm and, thereby, succeeds in consistently maximizing parsimony without reliance on untenable assumptions.