Schedule
A schedule of topics and readings is provided below. Each week will cover a single topic or group of topics. Monday lectures will typically be an introduction to the topic while Wednesday lectures will go into greater detail and involve some applications of the method. You should make sure to review the readings prior to that week’s lectures with an aim towards completing the reading assignments prior to Wednesday’s lecture.
Applications are optional but useful examples of a given methodological approach implemented in a published political science (or adjacent discipline) paper. I have tried to curate some examples for each week that provide a contrast between an “older” vs. a more “modern” paper using a particular methodology, highlight a debate over an interesting empirical question that hinges on a methodological problem, and/or just generally provide a cool empirical application using the framework being taught that week.
All hyperlinks to papers are either to the published version of the paper (if published) or to the working paper. You should use your university library access to obtain the full version if the article is not open access. Likewise, textbook readings will be to the free/open version of the book (if available) or to the UW-Madison university library eBook.
Week 1: Statistical Review
Wednesday, January 21
Topics:
- Estimands and estimators
- Review of statistical properties of estimators (bias/variance)
- Large sample theory, big-O/little-o notation
Readings:
- Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021. “What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory.” American Sociological Review 86 (3): 532–565.
- Torreblanca, Carolina, William Dinneen, Guy Grossman, and Yiqing Xu. 2025. “The Credibility Revolution in Political Science.” Working paper.
- Samii, Cyrus. 2023. “The ‘Problem Solving’ Approach and Social Science Methodology.” Blog post.
- Chapter 3; Blackwell, Matthew. 2025. A User’s Guide to Statistical Inference and Regression.
Week 2: Potential Outcomes + Randomized Experiments
Monday, January 26 & Wednesday, January 28
Topics:
- Counterfactual reasoning and the potential outcomes model
- What assumptions are needed to identify average treatment effects
- Why randomized experiments satisfy these assumptions
- Estimation and randomization inference in standard experimental designs
Readings:
- Chapter 1; Imbens, Guido W., and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press.
- Chapters 1–2; Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
- Sections 1–5; Athey, Susan, and Guido W. Imbens. 2017. “The Econometrics of Randomized Experiments.” In Handbook of Economic Field Experiments, vol. 1, edited by Abhijit Vinayak Banerjee and Esther Duflo, 73–140. Amsterdam: North-Holland.
- Druckman, James N., Donald P. Green, James H. Kuklinski, and Arthur Lupia. 2006. “The Growth and Development of Experimental Research in Political Science.” American Political Science Review 100 (4): 627–635.
Applications:
- Gerber, Alan S., Donald P. Green, and Christopher W. Larimer. 2008. “Social Pressure and Voter Turnout: Evidence from a Large-Scale Field Experiment.” American Political Science Review 102 (1): 33–48.
- Broockman, David E., and Joshua L. Kalla. 2025. “Consuming Cross-Cutting Media Causes Learning and Moderates Attitudes: A Field Experiment with Fox News Viewers.” Journal of Politics 87 (1): 246–261.
Week 3: Experiments: Attrition and Generalizability
Monday, February 2 & Wednesday, February 4
Topics:
- What do we do when things happen after the experiment (e.g. attrition)?
- Bounding treatment effects and partial identification.
- Generalizing experimental results beyond the sample ATE
Readings:
- Egami, Naoki, and Erin Hartman. 2023. “Elements of External Validity: Framework, Design, and Analysis.” American Political Science Review 117 (3): 1070–1088.
- Zhang, Junni L., and Donald B. Rubin. 2003. “Estimation of Causal Effects via Principal Stratification When Some Outcomes Are Truncated by ‘Death.’” Journal of Educational and Behavioral Statistics 28 (4): 353–368.
- Lee, David S. 2009. “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects.” Review of Economic Studies 76 (3): 1071–1102.
Applications:
- Bassan-Nygate, Lotem, Jonathan Renshon, Jessica L. P. Weeks, and Chagai M. Weiss. 2024. “The Generalizability of IR Experiments beyond the United States.” American Political Science Review 118 (4): 1–16.
- Dunning, Thad, Guy Grossman, Macartan Humphreys, Susan D. Hyde, Craig McIntosh, Gareth Nellis, Claire L. Adida, et al. 2019. “Voter Information Campaigns and Political Accountability: Cumulative Findings from a Preregistered Meta-Analysis of Coordinated Trials.” Science Advances 5 (7): eaaw2612.
Week 4: Experiments: Incorporating Covariates
Monday, February 9 & Wednesday, February 11
Topics:
- Stratification/blocking and using covariates in experiments
- Analysis of cluster-randomized experiments
- Introduction to regression estimators
Readings:
- Sections 6–12; Athey, Susan, and Guido W. Imbens. 2017. “The Econometrics of Randomized Experiments.” In Handbook of Economic Field Experiments, vol. 1, edited by Abhijit Vinayak Banerjee and Esther Duflo, 73–140. Amsterdam: North-Holland.
- Chapters 5–7; Blackwell, Matthew. 2025. A User’s Guide to Statistical Inference and Regression.
- Chapter 7; Imbens, Guido W., and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press.
- Lin, Winston. 2013. “Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman’s Critique.” Annals of Applied Statistics 7 (1): 295–318.
- Bonus: Samii, Cyrus, and P. M. Aronow. 2012. “On Equivalencies between Design-Based and Regression-Based Variance Estimators for Randomized Experiments.” Statistics & Probability Letters 82 (2): 365–370.
Applications:
- Nyhan, Brendan, and Jason Reifler. 2015. “The Effect of Fact-Checking on Elites: A Field Experiment on US State Legislators.” American Journal of Political Science 59 (3): 628–640.
- Raffler, Pia J. 2022. “Does Political Oversight of the Bureaucracy Increase Accountability? Field Experimental Evidence from a Dominant Party Regime.” American Political Science Review 116 (4): 1443–1459.
Week 5: Selection-on-Observables
Monday, February 16 & Wednesday, February 18
Topics:
- What to do when random assignment of treatment is not possible - common challenges of observational designs
- Assumptions behind “no unobserved confounding” designs
- Representing assumptions using graphical models
- Covariate adjustment using stratification
Readings:
- Chapter 12; Imbens, Guido W., and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press.
- Chapters 3, 6–8; Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
- Chapters 6–8; Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton: Chapman and Hall/CRC.
- Supplementary: Cinelli, Carlos, Andrew Forney, and Judea Pearl. 2024. “A Crash Course in Good and Bad Controls.” Sociological Methods & Research 53 (3): 1071–1104.
- Supplementary: 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 (1): 27–42.
Applications:
- Washington, Ebonya L. 2008. “Female Socialization: How Daughters Affect Their Legislator Fathers.” American Economic Review 98 (1): 311–332.
- Ba, Bocar A., Dean Knox, Jonathan Mummolo, and Roman Rivera. 2021. “The Role of Officer Race and Gender in Police-Civilian Interactions in Chicago.” Science 371 (6530): 696–702.
Week 6: Selection-on-Observables - Regression and Weighting
Monday, February 23 & Wednesday, February 25
Topics:
- Covariate adjustment using regression estimators
- Propensity scores and covariate adjustment via weighting
- Combining treatment and outcome models for a “doubly-robust” estimator
Readings:
- Imbens, Guido W. 2004. “Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review.” Review of Economics and Statistics 86 (1): 4–29.
- Glynn, Adam N., and Kevin M. Quinn. 2010. “An Introduction to the Augmented Inverse Propensity Weighted Estimator.” Political Analysis 18 (1): 36–56.
- Aronow, P. M., and Cyrus Samii. 2016. “Does Regression Produce Representative Estimates of Causal Effects?” American Journal of Political Science 60 (1): 250–267.
- Chapters 11–12; Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
Applications:
- Hübert, Ryan, and Ryan Copus. 2022. “Political Appointments and Outcomes in Federal District Courts.” Journal of Politics 84 (2): 908–922.
- Jares, Jake Alton, and Neil Malhotra. 2025. “Policy Impact and Voter Mobilization: Evidence from Farmers’ Trade War Experiences.” American Political Science Review 119 (2): 847–869.
Week 7: Selection-on-Observables - Matching
Monday, March 2 & Wednesday, March 4
Topics:
- Covariate adjustment using matching estimators
- Midterm exam on Wednesday March 4th
Readings:
- Rosenbaum, Paul R. 2020. “Modern Algorithms for Matching in Observational Studies.” Annual Review of Statistics and Its Application 7 (1): 143–176.
- Abadie, Alberto, and Guido W. Imbens. 2011. “Bias-Corrected Matching Estimators for Average Treatment Effects.” Journal of Business & Economic Statistics 29 (1): 1–11.
- Imbens, Guido W., and Yiqing Xu. 2025. “Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades after LaLonde (1986)?” Journal of Economic Perspectives 39 (4): 173–201.
Applications:
- Imai, Kosuke. 2005. “Do Get-Out-the-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments.” American Political Science Review 99 (2): 283–300.
- Hansen, Ben B., and Jake Bowers. 2009. “Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign.” Journal of the American Statistical Association 104 (487): 873–885.
Week 8: Instrumental Variables
Monday, March 9 & Wednesday, March 11
Topics:
- Identifying causal effects under unobserved confounding using exogenous variation in treatment induced by an instrument
- Estimation via the Wald estimator and two stage least squares (TSLS)
- Interpreting the IV estimand - the LATE theorem
Readings:
- Chapter 7; Cunningham, Scott. 2021. Causal Inference: The Mixtape. New Haven: Yale University Press.
- Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. 1996. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91 (434): 444–455.
- Andrews, Isaiah, James H. Stock, and Liyang Sun. 2019. “Weak Instruments in Instrumental Variables Regression: Theory and Practice.” Annual Review of Economics 11: 727–753.
- Słoczyński, Tymon. 2024. “When Should We (Not) Interpret Linear IV Estimands as LATE?” Working paper.
Applications:
- White, Ariel. 2019. “Misdemeanor Disenfranchisement? The Demobilizing Effects of Brief Jail Spells on Potential Voters.” American Political Science Review 113 (2): 311–324.
- Lal, Apoorva, Mackenzie Lockhart, Yiqing Xu, and Ziwen Zu. 2024. “How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on 67 Replicated Studies.” Political Analysis 32 (4): 521–540.
- Angrist, Joshua D., and Peter Hull. 2023. “Instrumental Variables Methods Reconcile Intention-to-Screen Effects across Pragmatic Cancer Screening Trials.” Proceedings of the National Academy of Sciences 120 (51): e2311556120.
Week 9: Differences-in-Differences
Monday, March 16 & Wednesday, March 18
Topics:
- Leveraging repeated outcomes over time to address unobserved confounding
- Assumptions behind the “differences-in-differences” strategy – parallel trends
- Estimation and diagnostics for the identification assumptions
- Connection to regression estimators with “two-way fixed effects”
Readings:
- Baker, Andrew, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, and Pedro H. C. Sant’Anna. 2025. “Difference-in-Differences Designs: A Practitioner’s Guide.” Working paper.
- Chapter 9; Cunningham, Scott. 2021. Causal Inference: The Mixtape. New Haven: Yale University Press.
- Chapter 3; de Chaisemartin, Clément, and Xavier D’Haultfœuille. 2023. Credible Answers to Hard Questions: Differences-in-Differences for Natural Experiments. Working paper.
Applications:
- Card, David, and Alan B. Krueger. 1994. “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania.” American Economic Review 84 (4): 772–793.
- Miller, Sarah, Norman Johnson, and Laura R. Wherry. 2021. “Medicaid and Mortality: New Evidence from Linked Survey and Administrative Data.” Quarterly Journal of Economics 136 (3): 1783–1829.
Week 10: Modern Differences-in-Differences
Monday, March 23 & Wednesday, March 25
Topics:
- Differences-in-differences under staggered adoption
- “New” DiD estimators - TWFE regression imputation vs. AIPW
- Diagnosing pre-trends violations and assessing robustness
- Relaxing parallel trends assumptions
Readings:
- Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe. 2023. “What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature.” Journal of Econometrics 235 (2): 2218–2244.
- Liu, Licheng, Ye Wang, and Yiqing Xu. 2024. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data.” American Journal of Political Science 68 (1): 160–176.
- Caetano, Carolina, and Brantly Callaway. 2024. “Difference-in-Differences When Parallel Trends Holds Conditional on Covariates.” Working paper.
- Rambachan, Ashesh, and Jonathan Roth. 2023. “A More Credible Approach to Parallel Trends.” Review of Economic Studies 90 (5): 2555–2591.
- Strezhnev, Anton. 2024. “Group-Specific Linear Trends and the Triple-Differences in Time Design.” Working paper.
Applications:
- Chiu, Albert, Xingchen Lan, Ziyi Liu, and Yiqing Xu. 2023. “Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study.” American Political Science Review 118 (4): 1–22.
Spring Break
No Class: Monday, March 23 & Wednesday, March 25
Week 11: Panel Data Causal Inference
Monday, April 6 & Wednesday, April 8
Topics:
- Estimating effects when past outcomes affect future treatments
- Estimators for effects of treatment histories
- Pitfalls and cautions with lagged outcome regressions
Readings:
- Blackwell, Matthew, and Adam N. Glynn. 2018. “How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables.” American Political Science Review 112 (4): 1067–1082.
- Xu, Yiqing. 2023. “Causal Inference with Time-Series Cross-Sectional Data: A Reflection.” In The SAGE Handbook of Research Methods in Political Science and International Relations, edited by Luigi Curini and Robert Franzese. London: SAGE.
- Daw, Jamie R., and Laura A. Hatfield. 2018. “Matching and Regression to the Mean in Difference-in-Differences Analysis.” Health Services Research 53 (6): 4138–4156.
Applications:
- Harvey, Cole J. 2022. “Who Delivers the Votes? Elected versus Appointed Local Executives, Election Manipulation, and Natural Support for Ruling Parties.” Electoral Studies 76: 102455.
- Bach, Laurent, Antoine Bozio, Arthur Guillouzouic, and Clément Malgouyres. 2023. “Dividend Taxes and the Allocation of Capital: Comment.” American Economic Review 113 (7): 2048–2052.
Week 12: Regression Discontinuity Designs
Monday, April 13 & Wednesday, April 15
Topics:
- Identification under unobserved confounding using quasi-random assignment at a cutpoint
- Local randomization vs. continuity-based approaches
- Estimation using local polynomial regression
- Diagnostics for design assumptions - “bunching” tests and placebo outcomes
Readings:
- Cattaneo, Matias D., Nicolás Idrobo, and Rocío Titiunik. 2019. A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge: Cambridge University Press.
- Cattaneo, Matias D., Nicolás Idrobo, and Rocío Titiunik. 2024. A Practical Introduction to Regression Discontinuity Designs: Extensions. Cambridge: Cambridge University Press.
- Marshall, John. 2024. “Can Close Election Regression Discontinuity Designs Identify Effects of Winning Politician Characteristics?” American Journal of Political Science 68 (2): 494–510.
- Hausman, Catherine, and David S. Rapson. 2018. “Regression Discontinuity in Time: Considerations for Empirical Applications.” Annual Review of Resource Economics 10 (1): 533–552.
Applications:
- Caughey, Devin, and Jasjeet S. Sekhon. 2011. “Elections and the Regression Discontinuity Design: Lessons from Close US House Races, 1942–2008.” Political Analysis 19 (4): 385–408.
- Eggers, Andrew C., Anthony Fowler, Jens Hainmueller, Andrew B. Hall, and James M. Snyder Jr. 2015. “On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from Over 40,000 Close Races.” American Journal of Political Science 59 (1): 259–274.
- De la Cuesta, Brandon, and Kosuke Imai. 2016. “Misunderstandings about the Regression Discontinuity Design in the Study of Close Elections.” Annual Review of Political Science 19 (1): 375–396.
Week 13: Mediation and Sensitivity Analysis
Monday, April 20 & Wednesday, April 22
Topics:
- Assessing the robustness of selection-on-observables results to hypothetical unobserved confounding.
- Decomposing causal effects into “direct” and “indirect” components
- Estimating mediation effects under ignorability assumptions
- Challenges and pitfalls of mediation analysis
Readings:
- Cinelli, Carlos, and Chad Hazlett. 2020. “Making Sense of Sensitivity: Extending Omitted Variable Bias.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82 (1): 39–67.
- Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto. 2011. “Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.” American Political Science Review 105 (4): 765–789.
- Bonus: Green, Donald P., Shang E. Ha, and John G. Bullock. 2010. “Enough Already about ‘Black Box’ Experiments: Studying Mediation Is More Difficult than Most Scholars Suppose.” Annals of the American Academy of Political and Social Science 628 (1): 200–208.
- Acharya, Avidit, Matthew Blackwell, and Maya Sen. 2016. “Explaining Causal Findings without Bias: Detecting and Assessing Direct Effects.” American Political Science Review 110 (3): 512–529.
- Blackwell, Matthew, Ruofan Ma, and Aleksei Opacic. 2024. “Assumption Smuggling in Intermediate Outcome Tests of Causal Mechanisms.” Working paper.
Applications:
- Rathbun, Brian C., Christopher Sebastian Parker, and Caleb Pomeroy. 2025. “Separate but Unequal: Ethnocentrism and Racialization Explain the ‘Democratic’ Peace in Public Opinion.” American Political Science Review 119 (2): 621–636.
- Tomz, Michael, and Jessica L. P. Weeks. 2025. “Race, Democracy, and Public Support for War.” American Political Science Review: 1–18.
- Hazlett, Chad. 2020. “Angry or Weary? How Violence Impacts Attitudes toward Peace among Darfurian Refugees.” Journal of Conflict Resolution 64 (5): 844–870.