Introduces a Bayesian approach to the use of causal models to design and carry out qualitative and mixed-methods research. Addressed to researchers across the social sciences, this book shows how causal models allow us to combine extensive and intensive data strategies to answer both general and case-specific causal questions.
Introduces a Bayesian approach to the use of causal models to design and carry out qualitative and mixed-methods research. Addressed to researchers across the social sciences, this book shows how causal models allow us to combine extensive and intensive data strategies to answer both general and case-specific causal questions.
Macartan Humphreys is Professor of Political Science at Columbia University and Director of the Institutions and Political Inequality group at the WZB Berlin, conducting research on post-conflict development, ethnic politics, and democratic decision-making. He has been President of the APSA Experimental Political Science section and Executive Director of the Evidence on Governance and Politics network.
Inhaltsangabe
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.
