Quality & Quantity

, Volume 49, Issue 2, pp 839–856

Parsimony and Causality

Article

Abstract

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 show that there is a tight conceptual connection between parsimony and causality: only maximally 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.

Keywords

Boolean method Set-theoretic method Qualitative Comparative Analysis (QCA) Coincidence Analysis (CNA) INUS causation Quine-McCluskey optimization 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of GenevaGenevaSwitzerland

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