Humphreys M., Jacobs A.M. Integrated Inferences: Causal Models for Qualitative and Mixed-Method Research

Cambridge University Press, 2023. — 435 p. — (Strategies for Social Inquiry). — ISBN 978-1-107-16962-3.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.Introduction
Foundations
Causal models
Illustrating causal models
Causal queries
Bayesian answers
Theories as causal models
Model-based causal inference
Process tracing with causal models
Process tracing applications
Integrated inferences
Integrated inferences applications
Mixing models
Design choices
Clue selection as a decision problem
Case selection
Going wide, going deep
Models in question
Justifying models
Evaluating models
Final words