Case selection and causal inferences in qualitative comparative research

Sykes, Bryan L. and Plümper, Thomas and Troeger, Vera E. and Neumayer, Eric (2019) Case selection and causal inferences in qualitative comparative research. PLOS ONE, 14 (7). e0219727. ISSN 1932-6203

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Traditionally, social scientists perceived causality as regularity. As a consequence, qualitative comparative case study research was regarded as unsuitable for drawing causal inferences since a few cases cannot establish regularity. The dominant perception of causality has changed, however. Nowadays, social scientists define and identify causality through the counterfactual effect of a treatment. This brings causal inference in qualitative comparative research back on the agenda since comparative case studies can identify counterfactual treatment effects. We argue that the validity of causal inferences from the comparative study of cases depends on the employed case-selection algorithm. We employ Monte Carlo techniques to demonstrate that different case-selection rules strongly differ in their ex ante reliability for making valid causal inferences and identify the most and the least reliable case selection rules.

Item Type: Article
Additional Information: Replication files for the Monte Carlo simulations can be accessed here: Troeger, Vera Eva; Plümper, Thomas; Neumayer, Eric, 2017, "Replication Data for: Case selection and causal inferences in qualitative comparative research", doi:10.7910/DVN/3H5EDP, Harvard Dataverse, V1.
Divisions: Departments > Sozioökonomie
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 20 Aug 2019 09:24
Last Modified: 25 Sep 2019 11:33
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