Published and Forthcoming Papers

Leavitt, T. (2023). Randomization-based, Bayesian inference of causal effects. Journal of Causal Inference, 11(1).
[Link to publisher’s site] [Download paper]

Leavitt, T. and V. Rivera-Burgos (2024). Audit experiments of racial discrimination and the importance of symmetry in exposure to cues. Political Analysis, 32(4), 445-462.
[Link to publisher’s site] [Download preprint] [Replication data]

Leavitt, T. Fisher Meets Bayes: The Value of Randomization for Bayesian Inference of Causal Effects. International Statistical Review (In press).
[Link to publisher’s site] [Download preprint] [Replication data]

Leavitt, T. and L. A. Hatfield. Averaged Prediction Models (APM): Identifying Causal Effects in Controlled Pre-post Settings with Application to Gun Policy. Accepted at The Annals of Applied Statistics.
[Download preprint] [Download supplement] [R package: CRAN, Github]

Under Review

Leavitt, T. Empirical Bayesian identification and inference for Difference-in-Differences. Revise and resubmit.

Leavitt, T. and L. Miratrix. A hands-on guide to design-based matching. Revise and resubmit.

Leavitt, T., J. Bowers, and L. Miratrix. Joint Sensitivity Analysis for Multiple Assumptions: Making Sense of Racial Disparity in Police Use of Force. Revise and resubmit.

Leavitt, T. and V. Rivera-Burgos. Towards More Reliable Message-based Experiments: Navigating the Mismeasurement of Intermediary Variables. Submitted.

Working Papers

Leavitt, T. and D. P. Green. The plausibility of experimental findings under selective reporting: An application to voter turnout experiments by proprietary organizations. Working paper.

Leavitt, T. and V. Rivera-Burgos. Parsing taste-based from statistical discrimination in audit experiments. Working paper.

Leavitt, T. and V. Rivera-Burgos. Bayesian learning from small but substantively important subgroups: An audit experiment among Black and Latino legislators. Working paper.

Book Chapters

Green, D. P., T. Leavitt, and D. Markovits (In Press). Challenges that Proprietary Research Poses for Meta-analysis. In J. M. Box-Steffensmeier, D. P. Christenson, and V. Sinclair-Chapman (Eds.), Oxford Handbook of Engaged Methodological Pluralism in Political Science, Volume 1. New York, NY: Oxford University Press.
[Link to publisher’s site] [Download preprint]

Bowers, J. and T. Leavitt (2020). Causality and design-based inference. In L. Curini and R. Franzese (Eds.), The SAGE Handbook of Research Methods in Political Science and International Relations, Volume 2, Chapter 41, pp. 769-804. Thousand Oaks, CA: SAGE Publications.
[Link to publisher’s site] [Download preprint]