| The use of differential privacy for census data and its impact on redistricting: The case of the 2020 US Census CT Kenny, S Kuriwaki, C McCartan, ETR Rosenman, T Simko, K Imai Science advances 7 (41), eabk3283, 2021 | 136 | 2021 |
| Sequential Monte Carlo for sampling balanced and compact redistricting plans C McCartan, K Imai The Annals of Applied Statistics 17 (4), 3300-3323, 2023 | 58 | 2023 |
| Widespread partisan gerrymandering mostly cancels nationally, but reduces electoral competition CT Kenny, C McCartan, T Simko, S Kuriwaki, K Imai Proceedings of the National Academy of Sciences 120 (25), e2217322120, 2023 | 46 | 2023 |
| Simulated redistricting plans for the analysis and evaluation of redistricting in the United States C McCartan, CT Kenny, T Simko, G Garcia III, K Wang, M Wu, S Kuriwaki, ... Scientific Data 9 (1), 689, 2022 | 35 | 2022 |
| Evaluating bias and noise induced by the US Census Bureau’s privacy protection methods CT Kenny, C McCartan, S Kuriwaki, T Simko, K Imai Science Advances 10 (18), eadl2524, 2024 | 22 | 2024 |
| The impact of the US Census Disclosure Avoidance System on redistricting and voting rights analysis CT Kenny, S Kuriwaki, C McCartan, E Rosenman, T Simko, K Imai arXiv preprint arXiv:2105.14197, 2021 | 20 | 2021 |
| redist: Simulation methods for legislative redistricting CT Kenny, C McCartan, B Fifield, K Imai The Comprehensive R Archive Network (CRAN) 3, 2021 | 19* | 2021 |
| Estimating Racial Disparities When Race is Not Observed C McCartan, R Fisher, J Goldin, DE Ho, K Imai NBER Working Papers, 32373, 2023 | 10 | 2023 |
| Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System CT Kenny, S Kuriwaki, C McCartan, ETR Rosenman, T Simko, K Imai Harvard Data Science Review, 2023 | 10 | 2023 |
| Making differential privacy work for census data users C McCartan, T Simko, K Imai Harv. Data Sci. Rev 10, 2023 | 7 | 2023 |
| 50-State Redistricting Simulations C McCartan, CT Kenny, T Simko, S Kuriwaki, G Garcia, K Wang, M Wu, ... Harvard Dataverse, 2021 | 5 | 2021 |
| adjustr: Stan model adjustments and sensitivity analyses using importance sampling C McCartan R package version 0.1 2, 2020 | 5 | 2020 |
| Recalibration of Predicted Probabilities Using the “Logit Shift”: Why Does It Work, and When Can It Be Expected to Work Well? ETR Rosenman, C McCartan, S Olivella Political Analysis 31 (4), 651-661, 2023 | 4 | 2023 |
| Finding Pareto efficient redistricting plans with short bursts C McCartan arXiv preprint arXiv:2304.00427, 2023 | 4 | 2023 |
| redistmetrics: Redistricting Metrics CT Kenny, C McCartan, B Fifield, K Imai The Comprehensive R Archive Network (CRAN), 2022 | 4* | 2022 |
| Researchers need better access to US Census data C McCartan, T Simko, K Imai Science 380 (6648), 902-903, 2023 | 3 | 2023 |
| Individual and differential harm in redistricting C McCartan, CT Kenny Working Paper, Harvard, University, 2022 | 3 | 2022 |
| PL94171: Tabulate P.L. 94-171 Redistricting Data Summary Files C McCartan, CT Kenny The Comprehensive R Archive Network (CRAN), 2021 | 3 | 2021 |
| Redistricting Reforms Reduce Gerrymandering by Constraining Partisan Actors C McCartan, CT Kenny, T Simko, E Ebowe, MY Zhao, K Imai arXiv preprint arXiv:2407.11336, 2024 | 2 | 2024 |
| Rejoinder: We can improve the usability of the census Noisy Measurements File C McCartan, T Simko, K Imai Harvard Data Science Review 6 (2), 2024 | 2 | 2024 |
Kosuke ImaiProfessor of Government and of Statistics, Harvard UniversityVerified email at Harvard.Edu
