
Thomas Leavitt
Asst Professor
Marxe School of Public and International Affairs
Department: Public Affairs
Areas of expertise:
Email Address: thomas.leavitt@baruch.cuny.edu
> View CV- Biography
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Dr. Leavitt is currently a postdoctoral research fellow at Harvard University, where he works on Difference-in-Differences and related statistical methods. He received his Ph.D. in Political Science from Columbia University, where he specialized in methodology and comparative politics. Dr. Leavitt’s research develops methods in causal inference, with a specific emphasis on randomized experiments, design-based inference, and their integration with Bayesian methodology. He applies these methodological developments to studies of racial and ethnic politics in a comparative perspective using original data from the US and South Africa.
Education
Ph.D., Political Science, Columbia University New York United States
M.Phil., Political Science, Columbia University New York United States
M.A., Political Science, Columbia University New York United States
M.A., Committee on International Relations, University of Chicago Chicago United States
B.A., Political Science, DePauw University Greencastle United States
Semester | Course Prefix | Course Number | Course Name |
---|---|---|---|
Spring 2025 | PAF | 9177 | Advanced Quantitative Methods |
Spring 2025 | PAF | 9272 | Causal Analysis and Inference |
Fall 2024 | PAF | 9177 | Advanced Quantitative Methods |
Fall 2024 | PAF | 9272 | Causal Analysis and Inference |
Spring 2024 | PAF | 9271 | Data Analysis for Public Servi |
Spring 2024 | PAF | 9272 | Causal Analysis and Inference |
Fall 2023 | PAF | 9271 | Data Analysis for Public Servi |
Fall 2023 | PAF | 9271 | Data Analysis for Public Servi |
Journal Articles
(2025). Joint Sensitivity Analysis for Multiple Assumptions: A Framework for Understanding Racial Disparity in Police Use of Force. Journal of the American Statistical Association,
(2025). Navigating the Mismeasurement of Intermediary Variables in Message-Based Experiments. Political Science Research and Methods,
(2025). Averaged Prediction Models (APM): Identifying Causal Effects in Controlled Pre-post Settings with Application to Gun Policy. Annals of Applied Statistics,
(2024). Audit Experiments of Racial Discrimination and the Importance of Symmetry in Exposure to Cues. Political Analysis, 32(4). 445-462.
(2024). The Matching Pipeline for Observational Research: A Guide from Design-Based Concepts to Step-by-Step Application. Observational Studies,
(2024). Fisher Meets Bayes: The Value of Randomisation for Bayesian Inference of Causal Effects. International Statistical Review,
(2023). An Empirical Bayes' Alternative to Design-based Identification and Inference for Difference-in-Differences. Observational Studies,
(2023). Randomization-based, Bayesian Inference of Causal Effects. Journal of Causal Inference, 11(1).
Book Chapters
(2023). Challenges that Proprietary Research Poses for Meta-Analysis. The Oxford Handbook of Methodological Pluralism Oxford University Press.
Leavitt, T., & Bowers, J. (2020). Causality and Design-Based Inference . The SAGE Handbook of Research Methods in Political Science and International Relations (p. 769–804). SAGE Publications.
Presentations
Leavitt, T. Audit Experiments of Racial Discrimination and the Importance of Symmetry in Exposure to Cues. Political Methodology Colloquium. : Department of Political Science, Columbia University.
Leavitt, T. Audit Experiments of Racial Discrimination and the Importance of Symmetry in Exposure to Cues. Data Science Cluster Meeting. : Baruch College.
Leavitt, T. Joint Sensitivity Analysis for Multiple Assumptions: Unpacking Racial Disparity in Police Use of Force. Statistics Winter Workshop. : Departments of Statistics and Economics, University of Florida.
Leavitt, T. Joint Sensitivity Analysis for Multiple Assumptions: Unpacking Racial Disparity in Police Use of Force. Methods Workshop. : Department of Political Science, Vanderbilt University.
Leavitt, T. Audit Experiments of Racial Discrimination and the Importance of Symmetry in Exposure to Cues. Political Science 5016: Field Experiments in Comparative Politics. : Department of Political Science, Washington University.
Leavitt, T. Parsing Taste-Based from Statistical Discrimination in Audit Experiments. Data Science Lunch Seminar Series. : The Center for Data Science, New York University.
Leavitt, T. Planning observational studies with unobserved confounding in mind. Joint Statistical Meetings. : American Statistical Association.
Leavitt, T. Model selection for Decreasing Dependence on Counterfactual, Identification Assumptions in Controlled Pre-Post Designs. Quantitative Methods Workshop. : Wilf Family Department of Politics, New York University.
Leavitt, T. Model selection for Decreasing Dependence on Counterfactual, Identification Assumptions in Controlled Pre-Post Designs. Applied Statistics Workshop. : The Institute for Quantitative Social Science, Harvard University.
Leavitt, T. Potential Outcome & Directed Acyclic Graphs (DAGs). The Miratrix C.A.R.E.S. Lab. : Graduate School of Education and Department of Statistics, Harvard University.
Leavitt, T. Major debates in African politics: Colonial rule, anti-colonial resistance and postindependence colonial legacies. B8772-001 — Global Immersion in East Africa. : Columbia Business School and Chazen Institute for Global Business, Columbia University.
Leavitt, T. Randomization-based, Bayesian Inference of Causal Effects. Quantitative Methods Workshop. : Wilf Family Department of Politics, New York University.
Other Scholarly Works
Leavitt, T., Hatfield, L., & Greifer, N. (2025). apm: Averaged Prediction Models. R package version 0.1.1.
Zeldow, B., Leavitt, T., & Hatfield, L. (2023). Difference-in-Differences (website).
College
Committee Name | Position Role | Start Date | End Date |
---|---|---|---|
Learning Assessment Committee | Committee Member | 8/31/2025 |