Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records (Supplementary Appendix, Simulations Appendix), with Ted Enamorado and Kosuke Imai (2019). American Political Science Review, Vol. 113, No. 2 (May), pp. 353-371.
A New Automated Redistricting Simulator Using Markov Chain Monte Carlo, with Michael Higgins, Kosuke Imai, and Alexander Tarr. Revise & Resubmit, Journal of Computational and Graphical Statistics.
Improving Model-Building Workflows for Predicting Heterogeneous Treatment Effects
Validating Ensembles of Simulated Redistricting Plans
fastLink: R Package for Fast Probabilistic Record Linkage, with Ted Enamorado and Kosuke Imai
Empirical and Computational Methods for Electoral Politics (2018). PhD Thesis, Department of Politics, Princeton University.
User’s Guide and Codebook for the ANES 2016 Time Series Voter Validation Supplemental Data, with Ted Enamorado and Kosuke Imai