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Replication Data for: Modeling Configurational Explanations

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Modeling Configurational Explanations
 
Identifier https://doi.org/10.7910/DVN/FORHNF
 
Creator Damonte, Alessia
 
Publisher Harvard Dataverse
 
Description 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.
 
Subject Computer and Information Science
Social Sciences
Explanation
inus causation
mediation
pruning
Qualitative Comparative Analysis
quasi-experimental designs
Structural Causal Model framework
 
Contributor Damonte, Alessia