Published and Forthcoming Papers

Leavitt, T. (2023). Randomization-based, Bayesian inference of causal effects. Journal of Causal Inference, 11(1), 20220025. DOI: 10.1515/jci-2022-0025
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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. DOI: 10.1017/pan.2024.3
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Leavitt, T. and L. A. Hatfield. (2025). Averaged Prediction Models (APM): Identifying Causal Effects in Controlled Pre-Post Settings with Application to Gun Policy. The Annals of Applied Statistics, 19(3), 1826-1846. DOI: 10.1214/25-AOAS2011
[Download paper] [Download supplement] [Replication Material: GitHub] [R package (apm): CRAN | GitHub]

Leavitt, T. (In press). Fisher Meets Bayes: The Value of Randomisation for Bayesian Inference of Causal Effects. International Statistical Review. DOI: 10.1111/insr.12598
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Under Review

Leavitt, T. Beyond Pre-Trends: A Concordance-Based Sensitivity Analysis for Difference-in-Differences. Revise and resubmit.

Leavitt, T. and L. Miratrix. Building a Design-Based Matching Pipeline: From Principles to Practical Implementation in R. Revise and resubmit.

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

Leavitt, T. and V. Rivera-Burgos. Navigating the Mismeasurement of Intermediary Variables in Message-Based Experiments. Minor revisions.

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. DOI: 10.1093/oxfordhb/9780192868282.013.21
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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. DOI: 10.4135/9781526486387.n44
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