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
- Teaching
- Research and Creative Activity
- Grants
- Honors and Awards
- Service
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 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
(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).
Audit experiments of racial discrimination and the importance of symmetry in exposure to cues . Political Analysis,
The Value of Randomization for Bayesian Belief Revision about Causal Hypotheses. International Statistical Review,
A hands-on guide to design-based matching . Observational Studies,
Predict, Correct, Select: A general strategy to identify causal effects of gun policy changes . Annals of Applied Statistics,
Audit Experiments of Racial Discrimination and the Importance of Symmetry in Exposure to Cues.
Book Chapters
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.
Challenges that proprietary research poses for meta-analysis. The Oxford Handbook of Methodological Pluralism
Presentations
Leavitt, T. Planning observational studies with unobserved confounding in mind . Joint Statistical Meetings. Toronto, CA: American Statistical Association.
Leavitt, T. Parsing Taste-Based from Statistical Discrimination in Audit Experiments . Data Science Lunch Seminar Series. : The Center for Data Science, New York University.
Rivera-Burgos, V., & Leavitt, T. (2024, May 25). Parsing Taste-Based from Statistical Discrimination in Audit Experiments. American Causal Inference Conference. Austin, Texas: The Society for Causal Inference.
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. 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. 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.
Leavitt, T. Joint Sensitivity Analysis for Multiple Assumptions: Unpacking Racial Disparity in Police Use of Force . : Departments of Statistics and Economics, University of Florida. In Progress.
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. : Department of Political Science, Washington University.
Leavitt, T. Audit experiments of racial discrimination and the importance of symmetry in exposure to cues . : Data Science Cluster, Baruch College.
College
| Committee Name | Position Role | Start Date | End Date |
|---|---|---|---|
| Learning Assessment Committee | Committee Member | 8/31/2025 |