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Modeling configurational explanations

Published online by Cambridge University Press:  16 February 2021

Alessia Damonte Open the ORCID record for Alessia Damonte [Opens in a new window]
Alessia Damonte*
Affiliation:
Università degli Studi di Milano, Milan, Italy
*
Corresponding author. Email: alessia.damonte@unimi.it
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Abstract

How can Qualitative Comparative Analysis contribute to causal knowledge? The article's answer builds on the shift from design to models that the Structural Causal Model framework has compelled in the probabilistic analysis of causation. From this viewpoint, models refine the claim that a ‘treatment’ has causal relevance as they specify the ‘covariates’ that make some units responsive. The article shows how QCA can establish configurational models of plausible ‘covariates’. It explicates the rationale, operations, and criteria that confer explanatory import to configurational models, then illustrates how the basic structures of the SCM can widen the interpretability of configurational solutions and deepen the dialogue among techniques.

Keywords

Explanationinus causationmediationpruningQualitative Comparative Analysisquasi-experimental designsStructural Causal Model framework
Type
Research Article
Information
Italian Political Science Review / Rivista Italiana di Scienza Politica , Volume 51 , Special Issue 2: Capturing Causation: Issues of Research Design in Political Science , July 2021 , pp. 182 - 197
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Società Italiana di Scienza Politica.

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