Integrated Inferences
Causal Models for Qualitative and Mixed-Method Research

Develops a new approach to the use of causal models for qualitative and mixed-method research design and causal inference.

Macartan Humphreys (Author), Alan M. Jacobs (Author)

9781107169623, Cambridge University Press

Hardback, published 30 November 2023

436 pages
25 x 17.5 x 2.5 cm, 0.975 kg

'With this illuminating book, Humphreys and Jacobs expand their pioneering approach to multi-method research, combining the Bayesian foundations of Lindley and Savage, the scaffolding of Neyman, Rubin, and Holland's causal reasoning, and the flexible architecture of Pearl and Glymour's DAG models. Social science scholars will find an engaging and accessible exposition and synthesis of ideas that are otherwise scattered across a sometimes daunting literature in statistics, computer science, philosophy, and political science.' Tasha Fairfield, London School of Economics and Andrew Charman, University of California, Berkeley

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.

1. Introduction
I. Foundations: 2. Causal models
3. Illustrating causal models
4. Causal queries
5. Bayesian answers
6. Theories as causal models
II. Model-based causal inference: 7. Process tracing with causal models
8. Process tracing applications
9. Integrated inferences
10. Integrated inferences applications
11. Mixing models
III. Design choices: 12. Clue selection as a decision problem
13. Case selection
14. Going wide, going deep
IV. Models in question: 15. Justifying models
16. Evaluating models
17. Final words
V. Appendices: 18. Causal Queries
19. Glossary
Bibliography
Index.

Subject Areas: Social research & statistics [JHBC]