Case selection and causal inferences in qualitative comparative research
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. Nowa...
| Main Authors: | , , , |
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| Format: | Others |
| Language: | en |
| Published: |
Public Library of Science
2019
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| Online Access: | http://epub.wu.ac.at/7105/1/file.pdf http://dx.doi.org/10.1371/journal.pone.0219727 |
| Summary: | 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. |
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