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Justin Grimmer
Justin Grimmer
Morris M. Doyle Centennial Professor. Senior Fellow, Hoover Institution. Stanford University
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
J Grimmer, BM Stewart
37242011
A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases
J Grimmer
Political Analysis 18 (1), 1-35, 2010
8312010
Representational style in Congress: What legislators say and why it matters
J Grimmer
Cambridge University Press, 2013
3702013
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
3202012
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
3042015
General purpose computer-assisted clustering and conceptualization
J Grimmer, G King
Proceedings of the National Academy of Sciences 108 (7), 2643, 2011
2672011
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
2592013
Machine learning for social science: An agnostic approach
J Grimmer, ME Roberts, BM Stewart
Annual Review of Political Science 24, 395-419, 2021
2162021
Money in exile: Campaign contributions and committee access
EN Powell, J Grimmer
The Journal of Politics 78 (4), 974-988, 2016
2152016
Text as data: A new framework for machine learning and the social sciences
J Grimmer, ME Roberts, BM Stewart
Princeton University Press, 2022
2142022
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
2012017
How to make causal inferences using texts
N Egami, CJ Fong, J Grimmer, ME Roberts, BM Stewart
Science Advances 8 (42), eabg2652, 2022
1812022
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
1692022
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
162*2014
Obstacles to estimating voter ID laws’ effect on turnout
J Grimmer, E Hersh, M Meredith, J Mummolo, C Nall
146*2017
An Introduction to Bayesian Inference via Variational Approximations
J Grimmer
Political Analysis 19 (1), 32, 2011
1392011
Congressmen in exile: The politics and consequences of involuntary committee removal
J Grimmer, EN Powell
The Journal of Politics 75 (4), 907-920, 2013
992013
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
932018
Current research overstates American support for political violence
SJ Westwood, J Grimmer, M Tyler, C Nall
Proceedings of the National Academy of Sciences 119 (12), e2116870119, 2022
832022
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