Quality & Quantity

, Volume 49, Issue 2, pp 839–856 | Cite as

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 

Notes

Acknowledgments

I am indebted to Alrik Thiem and Carsten Schneider for very valuable discussions about the arguments presented in this paper as well as for comments on previous drafts. Moreover, I thank an anonymous reviewer for a helpful report on an earlier version of the paper. Finally, I am grateful to the Swiss National Science Foundation for generous support of this work (grant PP00P1_144736/1).

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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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