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This course offers an advanced treatment of design issues in social science research that aims at causal inference, that is, at answering cause-and-effect questions of the general form: is a variable X a cause of an outcome Y ? If so, how large is the effect of X on Y ? And how do changes in X translate into changes in Y ? Starting from an exposition of the counterfactual model of causality and causal graphs, the course introduces the assumptions necessary for identifying various causal effects, and shows how and to what extent these assumptions are justified in different experimental and observational research designs. As to observational studies, the course gives an overview of common and new large-N methods for causal inference, such as regression and panel estimators, matching, instrumental variable and regression discontinuity designs. The course also discusses how the principles and methods introduced may be put to use in small-N settings and in studies which aim to parse the mechanisms underlying causal effects. The course’s primary aim is to provide students with the epistemological and methodological tools to critically evaluate existing empirical work and to develop research designs on their own that, to the greatest possible extent, strengthen the causal inferences made.
Course coordinator: Peter Selb, University of Konstanz Last update: Jusić Mirna, M.A., Ph.D. (26.02.2024)
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Aytaç, S. Erdem, Luis Schiumerini, and Susan Stokes. 2018. Why Do People Join Backlash Protests? Lessons from Turkey. Journal of Conflict Resolution 62: 1205-1228. Bechtel, Michael, and Jens Hainmüller. 2011. How Lasting is Voter Gratitude? An Analysis of the Short- and Long-term Electoral Returns to Beneficial Policy. American Journal of Political Science 55: 852-868. Becker, Sascha O., Peter H. Egger, and Maximilian von Ehrlich. 2010. Going NUTS: The Effect of EU Structural Funds on Regional Performance. Journal of Public Economics 94: 578-590. Chen, Jidong, Jennifer Pan, and Yiqing Xu. 2016. Sources of authoritarian responsiveness: A field experiment in China. American Journal of Political Science 60: 383-400. Fjelde, Hanne, and Hannah M. Smidt. 2022. Protecting the vote? Peacekeeping presence and the risk of electoral violence. British Journal of Political Science 52: 1113-1132 Holland, Paul W. 1986. Statistics and causal inference. Journal of the American Statistical Association 81: 945-960. Imai, Kosuke. 2005. Do get-out-the-vote calls reduce turnout? The importance of statistical methods for field experiments. American Political Science Review 99: 283-300. Keele, Luke, Randolph T. Stevenson, and Felix Elwert. 2020. The causal interpretation of estimated associations in regression models. Political Science Research & Methods 8: 1-13. Kern, Holger L., and Jens Hainmüller. 2009. Opium for the Masses: How Foreign Media Can Stabilize Authoritarian Regimes. Political Analysis 17: 377-399. King, Gary, Robert Keohane, und Sidney Verba. 1994. Designing Social Inquiry. Scientific Inference in Qualitative Research. Princeton University Press, ch. 3. Lesko, Catherine R., Buchanan, Ashley. L., Westreich, Daniel, Edwards, Jessie K., Hudgens, Michael G., and Cole, Stephen R. 2017. Generalizing study results. Epidemiology 28: 553-561. Masi, Tania, and Roberto Ricciuti. 2019 The heterogeneous effect of oil discoveries on democ[1]racy. Economics & Politics 31: 374-402 Rohrer, Julia M. 2018. Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science 1: 27-42. Last update: Jusić Mirna, M.A., Ph.D. (26.02.2024)
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