Advanced Quantitative Political Methodology: Causal Inference
Course Description
This course presents a modern approach to studying causal questions in political
science. In the first half of the course we introduce the core concepts of causality
and inference, how to do experiments and two basic frameworks for analyzing
data (linear regression and matching).
In the second part of the course we
look at different strategies for identifying causal effects using observational data.
Throughout, we will look at different applications of the methods we use, and we
will also give you a chance to apply the different methods in weekly assignments.
The overall goal of the course is to become a critical consumer of causal
claims in the social sciences and to give you the tools needed to do causal
inference in practice.
Class Activities
In the first class you will be split into study groups. You can use these study groups to complete the weekly assignments which are uploaded to Brightspace one week before each class.
Books
We will use longer excerpts from the following books
- Angrist, J. and J.S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Pres
- Stock J. H. & Watson, M. W. Introduction to Econometrics. 4th edition. Pearson, 2020 (SW).
- Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton
Class 1: What is a causal effect?
- Angrist, J. and J.S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Press. Chapters 1-2
- Samii, C. (2016). Causal empiricism in quantitative research. The Journal of Politics, 78(3), 941-955.
- Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42.
- Hariri, J. G. (2012). Kausal inferens i statskundskaben. Politica, 44(2), 184-201.
- Holland, Paul W. (1986) Statistics and Causal Inference. Journal of the American Statistical Association: 81(396), 945-960.
Class 2: Inference in Theory
- Wooldridge, J. M. (2013). Introductory econometrics: a modern approach (5th international ed.). Publisher South-Western Cengage Learning. Appendix C and Chapter 3.4.
- Heß, S. (2017). Randomization inference with Stata: A guide and software. The Stata Journal, 17(3), 630-651.
- Wooldridge, J. M. (2013). Introductory econometrics: a modern approach (5th international ed.). Publisher South-Western Cengage Learning. Appendix A-B.
Read Appendices A and B for good context on some of the notation and operators that we will use.
Class 3: Inference in Practice
- Simmons, J., Nelson, L., and Simonsohn, U. (2011). False-positive psychology: Undisclosed exibility in data collection and analysis allow presenting anything as significant. Psychological Science. 22, 1359-1366.
- Duflo, Esther, Abhijit Banerjee, Rachel Glennerster, and Michael Kremer. 2006. Section 4.1 "Sample size, design, and the power of experiments. Basic Principle" Using Randomization in Development Economics: A Toolkit. Handbook of Development Economics.
- Gelman, A., & Carlin, J. (2014). Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors. Perspectives on Psychological Science, 9, 641- 65
- Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349 (6251)
- Lenz, G., & Sahn, A. (2017). Achieving statistical signi cance with covariates and without transparency. Working paper.
- John P. A. Ioannidis, T. D. Stanley, Hristos Doucouliagos, The Power of Bias in Economics Research, The Economic Journal, Volume 127, Issue 605, October 2017, Pages F236{F265
Class 4: Designing Experiments
- McDermott, R. (2002). Experimental methods in political science. Annual Review of Political Science, 5(1), 31-61.
- Slothuus, R. (2016). Assessing the in uence of political parties on public opinion: The challenge from pretreatment e ects. Political Communication, 33(2), 302-327.
- Dafoe, A., Zhang, B. & Caughey, D. (2018) Information Equivalence in Survey Experiments. Political Analysis 26: 399-416.
- Deaton, A., & Cartwright, N. (2018). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine, 210, 2-21.
- Sniderman, P. M., & Grob, D. B. (1996). Innovations in experimental design in attitude surveys. Annual review of Sociology, 22(1), 377-399.
- Imbens, G. W. (2018). Comments On: Understanding and Misunderstanding Randomized Controlled Trails by Cartwright and Deaton. Stanford University, Graduate School of Business.
Class 5: Analyzing Experiments
- Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton. Chapters 2, 4 and 7
- Bullock, J. G., Green, D. P., & Ha, S. E. (2010). Yes, but what's the mechanism?(don't expect an easy answer). Journal of personality and social psychology, 98(4), 550.
- Enos, R. D. (2014). Causal e ect of intergroup contact on exclusionary attitudes. Proceedings of the National Academy of Sciences, 111(10), 3699-3704.
- Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton. Chapter 1.
Class 6: Analyzing Observational Data with Linear Regression
- Aronow, P. M., & Samii, C. (2016). Does regression produce representative estimates of causal e ects? American Journal of Political Science, 60(1), 250-267.
- Angrist, J. and J.S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Pres, Chapter 3.
- Wooldridge, J. M. (2013). Introductory econometrics: a modern approach (5th international ed.). Publisher South-Western Cengage Learning (pp.70-113). Chapter 6-7.
Class 7: Interactive Models & Mid-term Assignment
- Hainmueller, J., Mummolo, J., & Xu, Y. (2019). How much should we trust estimates from multiplicative interaction models? Simple tools to improve empirical practice. Political Analysis, 27(2), 163-192.
Class 8: Natural Experiments & Auxiliary Analyses
- Dunning, Thad (2008). Improving Causal Inference: Strengths and Limitations of Natural Experiments. Political Research Quarterly 61 (2): 282-293
- Susan Athey and Guido W. Imbens. 2017. The State of Applied Econometrics: Causality and Policy Evaluation Journal of Economic Perspectives|Volume 31, Number 2|Spring 2017. NB: only read "Supplementary Analyses" pp. 17-21.
- Erikson, R. S., & Stoker, L. (2011). Caught in the draft: The effects of Vietnam draft lottery status on political attitudes. American Political Science Review, 105(2), 221-237.
- Kokkonen, A. & Sundell, A. (2020). Leader Succession and Civil War. Comparative Political Studies, 53(3), 434-468.
- Mutz, D. C. 2011. pp. 131-154. Population-based survey experiments. Princeton University Press. (Blackboard)
Class 9: Difference-in-Differences
- Huntington-Klein, Nick. The Effect: An Introduction to Research Design and Causality, 2021. CRC Press, Chapter 18: Difference-in-Differences.
- Stock J. H. & Watson, M. W. Introduction to Econometrics. 3rd edition. Pearson, 2015 (SW). 1.3 (re-read part on types of data)
- Stock J. H. & Watson, M. W. Introduction to Econometrics. 3rd edition. Pearson, 2015 (SW). 13.4 (pp. 539-545)
- Bechtel, M. M., & Hainmueller, J. (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(4), 852-868.
Class 10: Panel Data & Fixed Effects
- Stock J. H. & Watson, M. W. Introduction to Econometrics. 3rd edition. Pearson, 2015 (SW). chapter 10
- Finkel, S. E., & Smith, A. E. (2011). "Civic education, political discussion, and the social transmission of democratic knowledge and values in a new democracy: Kenya 2002". American Journal of Political Science, 55(2), 417-435.
- Rabe-Hesketh, S. & A. Skrondal (2012) "Multilevel and Longitudinal Modeling Using Stata. 3rd edition." College Station: Stata Press. (s.73-97 + 123-147) (Blackboard).
- Angrist, J. and J.S. Pischke, Mostly Harmless Econometrics, Princeton University Press, 2009 (MHE). Chapter 5.
Class 11: Interrupted Time Series & Synthetic Control
- Shadish, W.R., Cook, T.D., & Campbell, D.T (2002) "Interrupted Time Series" Kapitel 6 fra Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton-Mifflin. (blackboard)
- Abadie, A., A. Diamond and J. Hainmueller (2015) Comparative Politics and the Synthetic Control Method American Journal of Political Science, April 2015, 59(2), 495-510.
- Abadie, A., A. Diamond and J. Hainmueller (2010), Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program," Journal of the American Statistical Association, vol. 105, 493-505.
- Hansen, B. T., Østergaard, S. D., SØnderskov, K. M., & Dinesen, P. T. (2016). Increased incidence rate of trauma-and stressor-related disorders in Denmark after the September 11, 2001, terrorist attacks in the United States. American Journal of Epidemiology, 184(7), 494-500.
Class 12: Instrumental Variables
- Angrist, J. and J.S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Pres, Chapter 4.
- Stock J. H. & Watson, M. W. Introduction to Econometrics. 3rd edition. Pearson, 2015 (SW). chapter 12
- Hariri, J. G. (2012). The autocratic legacy of early statehood. American Political Science Review, 106(3), 471-494.
Class 13: Regression Discontinuity Design
- Angrist, J. and J.S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Pres, Chapter 6.
- Titiunik, R. & Skovron, C. (2017) A Practical Guide to Regression Discontinuity Designs in Political Science. Pages 1-47. Unpublished Manuscript.
- Hall, A. B. (2015). What happens when extremists win primaries? American Political Science Review, 109(1), 18-42.
Class 14: Matching
- Elizabeth A. Stuart 2010 Matching Methods for Causal Inference: A Review and a Look Forward Statistical Science, Vol. 25, No. 1, 1-29. Following sections are not part of the curriculum: 3.2.3 Weighting adjustments, 4.1 Numerical diagnostics, 6 Discussion
- Kam, C. D., & Palmer, C. L. (2008). Reconsidering the effects of education on political participation. The Journal of Politics, 70(3), 612-631.
- Imbens, Guido. 2014. Matching Methods in Practice: Three Examples. NBER Working Paper 19959.
- Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart. 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference." Political Analysis, 15: 199-236.
Class 15: Review and Exam Preparation
- No readings.
