Experimental Political Science and the Study of Causality

Part I. Introduction: 1. The advent of experimental political science; Part II. Experimental Reasoning about Causality: 2. Experiments and causal relations; 3. The causal inference problem and the Rubin causal model; 4. Controlling observables and unobservables; 5. Randomization and pseudo-randomization; 6. Formal theory and causality; Part III. What Makes a Good Experiment?: 7. Validity and experimental manipulations; 8. Location, artificiality, and related design issues; 9. Choosing subjects; 10. Subjects' motivations; 11. History of codes of ethics and human subjects research; 12. Ethical decision making and political science experiments; 13. Deception in experiments; 14. The future of experimental political science; 15. Appendix: the experimentalist's to do list.

Rebecca B. Morton is a Professor in the Wilf Family Department of Politics at New York University. She received her Ph.D. from Tulane University and has held academic positions at Tulane, Texas A & M University, University of Iowa, University of California San Diego, and University of Houston. She was a Visiting Scholar at the Center for the Study of Democratic Politics at the Woodrow Wilson School at Princeton University, the Russell Sage Foundation, and the Hanse-Wissenschaftkolleg in Delmenhorst, Germany. Her book Learning by Voting: Sequential Choices in Presidential Primaries and Other Elections (with Kenneth Williams, 2001) addresses the effects of voting sequentially, as in presidential primaries in the United States. Her more recent book, Analyzing Elections (2006), is a comprehensive study of the American electoral process. Morton also considered the complexity of empirical evaluation of formal models in her book Methods and Models: A Guide to the Empirical Analysis of Formal Models in Political Science (Cambridge University Press, 1999). Her research has appeared in the American Economic Review, the American Journal of Political Science, the American Political Science Review, the Journal of Law and Economics, the Review of Economics and Statistics, and the Review of Economic Studies. Kenneth C. Williams is currently a Professor of Political Science at Michigan State University. He received his Ph.D. from the University of Texas at Austin and did a postdoctoral fellowship at Massachusetts Institute of Technology. He was a Visiting Professor at the University of California at Santa Barbara and also taught several summer courses at Birkbeck, University of London. He has published articles in the American Political Science Review, the American Journal of Political Science, Experimental Economics, Economics and Politics, the Journal of Theoretical Politics, and Public Choice. He is also co-author of Learning by Voting (with Rebecca Morton, 2001).

'This is a landmark contribution - not only in what it offers for experimentalists but for social science in general. Morton and Williams present a distinctive approach to how to conduct research that is sure to be widely discussed and debated.' James N. Druckman, Northwestern University 'This path-breaking work is the first political science monograph to cover laboratory, survey, and field experimentation. Using a wealth of examples from a wide array of subfields, Morton and Williams cover topics from causal inference to research ethics in a lively and engaging manner.' Donald Green, Yale University 'Morton and Williams's review of experimental methodology and reasoning in political science will be the benchmark reference for experimental methodology in political science for years to come. It is comprehensive in its discussion of methods, scientific reasoning, and ethics, and at the same time it tears down boundaries across subfields of political science and across different approaches to experimental research in the discipline. The authors successfully argue for and carefully lay out discipline-wide standards for experimental methodology in political science. The framework provided can be fruitfully used by those who conduct lab, field, or survey experiments as well as those who use experimental reasoning with observational data.' Thomas Palfrey, California Institute of Technology