Natural Experiments in the Social Sciences

Dunning, Thad
Thad Dunning is Associate Professor of Political Science at Yale University and a research fellow at Yale's Institution for Social and Policy Studies and the Whitney and Betty MacMillan Center for International and Area Studies. He has written on a range of methodological topics, including impact evaluation, econometric corrections for selection effects and multi-method research in the social sciences, and his first book, Crude Democracy: Natural Resource Wealth and Political Regimes (Cambridge University Press, 2008), won the Best Book Award from the Comparative Democratization Section of the American Political Science Association.

This unique book is the first comprehensive guide to the discovery, analysis, and evaluation of natural experiments - an increasingly popular methodology in the social sciences. Thad Dunning provides an introduction to key issues in causal inference, including model specification, and emphasizes the importance of strong research design over complex statistical analysis. Surveying many examples of standard natural experiments, regression-discontinuity designs, and instrumental-variables designs, Dunning highlights both the strengths and potential weaknesses of these methods, aiding researchers in better harnessing the promise of natural experiments while avoiding the pitfalls. Dunning also demonstrates the contribution of qualitative methods to natural experiments and proposes new ways to integrate qualitative and quantitative techniques. Chapters complete with exercises and appendices covering specialized topics such as cluster-randomized natural experiments, make this an ideal teaching tool as well as a valuable book for professional researchers.

The first comprehensive guide to natural experiments, providing an ideal introduction for scholars and students. This book provides scholars and students with the first comprehensive guide to the use and evaluation of natural experiments an increasingly popular methodology in the social sciences. It introduces the key issues in causal inference, including model specification, and emphasizes the importance of strong research design over complex statistical analysis.

1. Introduction: why natural experiments?; Part I. Discovering Natural Experiments: 2. Standard natural experiments; 3. Regression-discontinuity designs; 4. Instrumental-variables designs; Part II. Analyzing Natural Experiments: 5. Simplicity and transparency: keys to quantitative analysis; 6. Sampling processes and standard errors; 7. The central role of qualitative evidence; Part III. Evaluating Natural Experiments: 8. How plausible is as-if random?; 9. How credible is the model?; 10. How relevant is the intervention?; Part IV. Conclusion: 11. Building strong research designs through multi-method research.