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oai:figshare.com:article/33802801 2026-09-15T15:26:27Z portal_1197 item_type_3 month_year_09_2026

Data for Case selection and causal inferences in qualitative comparative research Vera Troeger (24805879) Thomas Plümper (3902578) Eric Neumayer (4899055) Sociology -- Research Social sciences -- Comparative method Causation Social sciences -- Research -- Methodology Case method Migrated from ePrints 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.<br><br>Data record consists of a single data file in .do format. This data file contains the STATA do-file for the Monte Carlo experiments conducted in the paper. 2017-11-12T00:00:00Z Dataset Dataset 10.82444/warw.33802801.v1 https://figshare.com/articles/dataset/Data_for_Case_selection_and_causal_inferences_in_qualitative_comparative_research/33802801 CC BY 4.0