Qualitative Causal Inference and Explanation

Nyhet

Inbunden, Engelska, 2026

1 489 kr

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While existing qualitative, case-study methods deliver specific explanations, quantitative approaches to causal inference emphasize valid inferences at the expense of explanations. In this book, David Waldner presents a hybrid method drawing on both approaches to ensure that explanations are based on validly inferred causes and to avoid making valid inferences that have limited explanatory power. Qualitative Causal Inference and Explanation integrates a qualitative identification strategy based on graph-theoretic analysis into traditional process-tracing methods by introducing three novel methodological concepts: hypothetical interventions, invariant causal mechanisms, and event-history maps. This new approach provides clear and feasible standards for making valid, unit-level causal inferences. The result is a groundbreaking approach to explaining complex social and political phenomena, one that better avoids false positives while providing explanations that satisfy the criteria of explanatory depth, density, relevance, and unification.

Produktinformation

Utgivningsdatum2026-06-30
Vikt500 g
FormatInbunden
SpråkEngelska
SerieStrategies for Social Inquiry
Antal sidor380
FörlagCambridge University Press
ISBN9781009769563

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Sociologi inom Samhalle och politik

David Waldner is Associate Professor in the Department of Politics at the University of Virginia where he studies long-term political and economic development using case-study methods. He is the author of State Building & Late Development (Cornell University Press, 1999) and Rethinking the Resource Curse (with Benjamin Smith, Cambridge University Press, 2021), as well as many articles and book chapters on the dynamics of political regime change and qualitative methods.

'Waldner equips qualitative researchers with a shared language for making causal explanations explicit, debatable, and cumulative across cases, subfields, disciplines, and generations. The payoff of rigorous standards, he shows, is liberation, not constraint: scholars gain the questions they must ask of one another's work-and their own-to deepen what we know.' Jake Bowers, Professor in the Departments of Political Science and Statistics, University of Illinois at Urbana-Champaign