Integrated Inferences

Causal Models for Qualitative and Mixed-Method Research

Bibliographic Data

ID19907566
AuthorsMichael Humphreys (0000-0001-7029-2326, WZB Berlin Social Science Center), Macartan Humphreys, Alan M Jacobs (0000-0002-1008-2551, University of British Columbia)
Year2023
Publication date2023-10-31
Open AccessYes
TypeBOOK
VenueIntegrated Inferences (SOURCE_BOOK)
PublisherCambridge University Press (PUBLISHER • US)
DOI10.1017/9781316718636
OpenAlexW4387566592
ISBN9781316718636
Open LibraryOL57566046M
LanguageEN
Citations received9

There is a growing consensus in the social sciences on the virtues of research strategies that combine quantitative with qualitative tools of inference. Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are able to integrate information from different data sources while connecting theory and empirics in a far more systematic and transparent manner than standard qualitative and quantitative approaches allow. This book provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two. The authors also demonstrate how causal models can guide research design, informing choices about which cases, observations, and mixes of methods will be most useful for addressing any given question

Bayesian inference · Bayesian network · Bayesian probability · Causal inference · Causal model · Data science · Econometrics · Inference · Management science · Process (computing) · Process tracing · Statistics · Artificial Intelligence · Computer Science · Engineering · Mathematics · Qualitative Comparative Analysis Research · Research · Social sciences

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