| Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts J Grimmer, BM Stewart | 3607 | 2011 |
| A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases J Grimmer Political Analysis 18 (1), 1-35, 2010 | 814 | 2010 |
| Representational style in Congress: What legislators say and why it matters J Grimmer Cambridge University Press, 2013 | 360 | 2013 |
| How words and money cultivate a personal vote: The effect of legislator credit claiming on constituent credit allocation J Grimmer, S Messing, SJ Westwood American Political Science Review 106 (4), 703-719, 2012 | 314 | 2012 |
| We are all social scientists now: How big data, machine learning, and causal inference work together J Grimmer PS: Political Science & Politics 48 (1), 80-83, 2015 | 292 | 2015 |
| General purpose computer-assisted clustering and conceptualization J Grimmer, G King Proceedings of the National Academy of Sciences 108 (7), 2643, 2011 | 262 | 2011 |
| Appropriators not position takers: The distorting effects of electoral incentives on congressional representation J Grimmer American Journal of Political Science 57 (3), 624-642, 2013 | 255 | 2013 |
| Money in exile: Campaign contributions and committee access EN Powell, J Grimmer The Journal of Politics 78 (4), 974-988, 2016 | 214 | 2016 |
| Machine learning for social science: An agnostic approach J Grimmer, ME Roberts, BM Stewart Annual Review of Political Science 24, 395-419, 2021 | 193 | 2021 |
| Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods J Grimmer, S Messing, SJ Westwood Political Analysis 25 (4), 413-434, 2017 | 190 | 2017 |
| Are Close Elections Random? J Grimmer, E Hersh, B Feinstein, D Carpenter Unpublished manuscript, 2011 | 167* | 2011 |
| The impression of influence: legislator communication, representation, and democratic accountability J Grimmer, SJ Westwood, S Messing Princeton University Press, 2014 | 165* | 2014 |
| How to make causal inferences using texts N Egami, CJ Fong, J Grimmer, ME Roberts, BM Stewart Science Advances 8 (42), eabg2652, 2022 | 164* | 2022 |
| Text as data: A new framework for machine learning and the social sciences J Grimmer, ME Roberts, BM Stewart Princeton University Press, 2022 | 158 | 2022 |
| Obstacles to estimating voter ID laws’ effect on turnout J Grimmer, E Hersh, M Meredith, J Mummolo, C Nall | 136* | 2017 |
| An Introduction to Bayesian Inference via Variational Approximations J Grimmer Political Analysis 19 (1), 32, 2011 | 136 | 2011 |
| Causal inference in natural language processing: Estimation, prediction, interpretation and beyond A Feder, KA Keith, E Manzoor, R Pryzant, D Sridhar, Z Wood-Doughty, ... Transactions of the Association for Computational Linguistics 10, 1138-1158, 2022 | 128 | 2022 |
| Congressmen in exile: The politics and consequences of involuntary committee removal J Grimmer, EN Powell The Journal of Politics 75 (4), 907-920, 2013 | 97 | 2013 |
| Mirrors for princes and sultans: advice on the art of governance in the medieval Christian and Islamic worlds L Blaydes, J Grimmer, A McQueen The Journal of Politics 80 (4), 1150-1167, 2018 | 85 | 2018 |
| TopicCheck: Interactive alignment for assessing topic model stability J Chuang, ME Roberts, BM Stewart, R Weiss, D Tingley, J Grimmer, ... Proceedings of the 2015 Conference of the North American Chapter of the …, 2015 | 77 | 2015 |
Brandon M. StewartPrinceton UniversityVerified email at princeton.edu
