Research
Current projects
Publications
Publications in Refereed Journals
- Ratkovic, M. (2023). Relaxing assumptions, improving inference: Integrating machine learning and the linear regression. American Political Science Review, 117, 1053-1069.
- Ratkovic, M. and Tingley, D. (2023). Estimation and inference on nonlinear and heterogeneous effects. The Journal of Politics : JOP, 85, 421–435.
- Enamorado, T., López-Moctezuma, G. and Ratkovic, M. (2021). Scaling data from multiple sources. Political Analysis : PA, 29, 212–235.
- Kim, I. S., Londregan, J. and Ratkovic, M. (2019). The effects of political institutions on the extensive and intensive margins of trade. International Organization : IO, 73, 755–792.
- Kim, I. S., Londregan, J. and Ratkovic, M. (2018). Estimating spatial preferences from votes and text. Political Analysis : PA, 26, 210–229.
- Ratkovic, M. and Tingley, D. (2017). Sparse estimation and uncertainty with application to subgroup analysis. Political Analysis : PA, 25, 1–40.
- Imai, K. and Ratkovic, M. (2015). Robust estimation of inverse probability weights for marginal structural models. Journal of the American Statistical Association : JASA, 110, 1013-1023.
- Imai, K. and Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society. Series B, Statistical Methodolgy, 76, 243–263.
- Imai, K. and Ratkovic, M. (2013). Estimating treatment effect heterogeneity in randomized program evaluation. The Annals of Applied Statistics, 7, 443–470.
- Ratkovic, M. and Eng, K. H. (2010). Finding jumps in otherwise smooth curves: Identifying critical events in political processes. Political Analysis : PA, 18, 57–77.
Chapters in Edited Volumes
- Ratkovic, M. (2021). Subgroup analysis: Pitfalls, promise, and honesty. In , Advances in experimental political science (S. 271–288). , Cambridge University Press: Cambridge [u. a.].
Working Papers
- “Improving Variable Importance Measures.” With Zenobia Chan.
- “Sparse Multilevel Regression and Poststratification.” With Max Goplerud, Shiro Kuriwaki, and Dustin Tingley.
- “Bicoordinate Descent for the LASSO.” With In Song Kim and John Londregan.
Published Software
- “PLCE: R package for Semiparametrically Efficient Causal Effect Estimation with Binary and Continuous Treatment Variables.” Available through the Comprehensive R Archive Network.
- “sparsereg: R package for Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data.” With Dustin Tingley. Available through the Comprehensive R Archive Network.
- “CBPS: R Package for Covariate Balancing Propensity Score.” With Kosuke Imai and Christian Fong. Available through the Comprehensive R Archive Network.
- “FindIt: R Package for Estimating Heterogeneous Treatment Effects.” With Kosuke Imai. Available through the Comprehensive R Archive Network.
Grants and Awards
- Minerva Research Initiative Grant. “Reforging Identity: Comparative Ethnographic Studies of Influence Operations in Taiwan and Estonia.” $852,031.
- Stanley Kelley, Jr. Award for Excellence in Teaching. Department of Politics, Princeton University. May 12, 2021.
- Gosnell Prize for Excellence in Political Methodology, for “Sparse Estimation with Uncertainty: Subgroup Analysis in Large Dimensional Designs.” With Dustin Tingley. July 22, 2016.
- Thomas R. Ten Have Memorial Award for Best Poster, for “Identification of Treatment Effect Heterogeneity as a Variable Selection Problem.” With Kosuke Imai. Atlantic Causal Inference Conference, Ann Arbor. May 20, 2011.
- Nomination for Best Paper, for “Identification of Treatment Effect Heterogeneity as a Variable
- Selection Problem.” With Kosuke Imai. Annual Midwest Political Science Association Meeting, Chicago. April 1, 2011.
- Department of Political Science Teaching Award. University of Wisconsin-Madison. Fall, 2007.
Invited Talks
- “Using LLMs for Regression and ANOVA: Statistical Inference and Testing of Text Data with Large Language Models”. With Haoyu Zhai. 7th International and Interdisciplinary Conference on the Quantitative and Computational Analysis of Textual Data (COMPTEXT), Vienna / Austria. (April 26, 2025)
- “Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression.” Polmeth Europe 2025, London / UK. (April 7, 2025)
- Invited talk at the Aix-Marseille School of Economics, Marseille / France. (March 24, 2025)
- Invited speaker at the Panel discussion on the US elections organized by the student representatives of the Departments of Sociology and Political Science of the University of Mannheim, Mannheim / Germany (October 22, 2024).
- Invited speaker at the plenary panel Community insights on AI readiness. STM Annual Conference 2024 – Advancing trusted research in the AI era, Frankfurt a.M. / Germany. (October 14, 2024)
- “Context Augmentation for the Statistical Analysis of Text.” With Haoyu Zhai. 6th International and Interdisciplinary Conference on the Quantitative and Computational Analysis of Textual Data (COMPTEXT), Amsterdam / Netherlands. (May 4, 2024)
- “Estimation and Inference on Nonlinear and Heterogeneous Effects.” International Conference on Machine Learning (ICML). With Dustin Tingley. Virtual conference. (September 10, 2021)
- “Rehabilitating the Regression: Honest and Valid Causal Inference through Machine Learning.” University of Michigan ISQM Seminar, Ann Arbor, MI / U.S.A. (November 4, 2019)
- “Rehabilitating the Regression: Honest and Valid Causal Inference through Machine Learning.” New York University Methods Seminar, New York, NY / U.S.A. (October 28, 2019)
- “Rehabilitating the Regression: Honest and Valid Causal Inference through Machine Learning.” Duke University Methods Seminar, Durham, NC / U.S.A. (May 13, 2019)
- “The Effects of Political Institutions on the Extensive and Intensive Margins of Trade.” V-Dem Institute, Gothenburg / Sweden. (May 20, 2018)
- “Causal Inference through the Method of Direct Estimation.” Ulric B. and Evelyn L. Bray Social Science Seminar Series, California Institute of Technology, Pasadena, CA / U.S.A. (February 26, 2018)
- “Causal Inference through the Method of Direct Estimation.” Yale Methods Seminar, Yale University, New Haven, CT / U.S.A. (March 8, 2017)
- “Causal Inference through the Method of Direct Estimation.” IQSS, Harvard University, Cambridge, MA / U.S.A. (April 27, 2017)
- “What If? Machine Learning for Causal Inference.” Harvard University and MIT, Cambridge, MA / U.S.A. (February 2016)
- “A Sparse Decomposition of Democracy and Trade”. Panel discussion, Annual Summer Meeting, Society for Political Methodology, University of Georgia, Athens, GA / U.S.A. (July 26, 2014)
- Summer School in Quantitative Methods, Empirical Political Economy Network. CIDE, Mexico City / Mexico. (May 19–20, 2014)
- “Covariate Balancing Propensity Score.” Panel discussion, Annual Meeting of the Society for Political Methodology. University of North Carolina-Chapel Hill, Chapel Hill, NC / U.S.A. (July 19, 2012)
- Machine Learning Seminar. Department of Computer Science, Princeton University, Princeton, NJ / U.S.A. (March 14, 2012)
- “Achieving Optimal Covariate Balance Under General Treamtent Regimes.” Thomas R. Ten Have Memorial Presentation. Atlantic Causal Inference Conference, Johns Hopkins University, Baltimore, MD / U.S.A. (May 25, 2012)
- Experiments Seminar. Institution for Social and Policy Studies, Yale University, New Haven, CT / U.S.A. (September 8, 2011)
- “Achieving Optimal Covariate Balance Under General Treatment Regimes.” Annual Meeting of the Society for Political Methodology. Princeton University, Princeton, NJ / U.S.A. (July 29, 2011)
- Annual Meeting of the Midwest Political Science Association. Chicago, IL / U.S.A. (April 1, 2011)
- “Identifying the Effects of Political Boundaries: Simultaneous Variable Selection and Curve Fitting through Mixed-Penalty Regularization.” New Faces in Political Methodology Conference. Quantitative Social Science Initiative, Pennsylvania State University, University Park, PA /U.S.A. (May 1, 2010)