Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- Part I Foundations
- Part 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
- Part III Design Choices
- Part IV Models in Question
- Part V Appendices
- Bibliography
- Index
11 - Mixing Models
from Part II - Model-Based Causal Inference
Published online by Cambridge University Press: 13 October 2023
Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- Part I Foundations
- Part 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
- Part III Design Choices
- Part IV Models in Question
- Part V Appendices
- Bibliography
- Index
Summary
This chapter shows how we can integrate inferences across models. We provide four examples of situations in which, by combining models, researchers can learn more than they could from any single model. Examples include situations in which researchers seek to integrate inferences from experimental and observational data, learn across settings, or integrate inferences from multiple studies.
- Type
- Chapter
- Information
- Integrated InferencesCausal Models for Qualitative and Mixed-Method Research, pp. 281 - 298Publisher: Cambridge University PressPrint publication year: 2023