| Covariate balancing propensity score K Imai, M Ratkovic Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2014 | 1428 | 2014 |
| Estimating treatment effect heterogeneity in randomized program evaluation K Imai, M Ratkovic | 796 | 2013 |
| Robust estimation of inverse probability weights for marginal structural models K Imai, M Ratkovic Journal of the American Statistical Association 110 (511), 1013-1023, 2015 | 122 | 2015 |
| Sparse estimation and uncertainty with application to subgroup analysis M Ratkovic, D Tingley Political Analysis 25 (1), 1-40, 2017 | 97 | 2017 |
| CBPS: covariate balancing propensity score C Fong, M Ratkovic, K Imai, C Hazlett, X Yang, S Peng, I Lee CRAN: Vienna, Austria, 2019 | 62 | 2019 |
| Estimating spatial preferences from votes and text IS Kim, J Londregan, M Ratkovic Political Analysis 26 (2), 210-229, 2018 | 40 | 2018 |
| CBPS: R package for covariate balancing propensity score C Fong, M Ratkovic, K Imai Comprehensive R Archive Network (CRAN), 2014 | 40 | 2014 |
| Sparse Multilevel Regression (and Poststratification (sMRP)) M Goplerud, S Kuriwaki, M Ratkovic, D Tingley Unpublished manuscript, Harvard University 723, 2018 | 22 | 2018 |
| The effects of political institutions on the extensive and intensive margins of trade IS Kim, J Londregan, M Ratkovic International Organization 73 (4), 755-792, 2019 | 20 | 2019 |
| Subgroup analysis: pitfalls, promise, and honesty M Ratkovic, JN Druckman, DP Green Advances in Experimental Political Science, 271-88, 2021 | 18 | 2021 |
| Voting, Speechmaking, and the Dimensions of Conflict in the US Senate IS Kim, J Londregan, M Ratkovic Annual Meeting of the Midwest Political Science Association, 2014 | 18 | 2014 |
| Balancing within the margin: Causal effect estimation with support vector machines M Ratkovic Department of Politics, Princeton University, Princeton, NJ, 2014 | 14 | 2014 |
| Finding jumps in otherwise smooth curves: Identifying critical events in political processes MT Ratkovic, KH Eng Political Analysis 18 (1), 57-77, 2010 | 14 | 2010 |
| Causal inference through the method of direct estimation M Ratkovic, D Tingley arXiv preprint arXiv:1703.05849, 2017 | 12 | 2017 |
| Package ‘cbps’ C Fong, M Ratkovic, K Imai, C Hazlett | 8 | 2022 |
| Identifying the largest balanced subset of the data under general treatment regimes M Ratkovic Princeton University, 2012 | 7 | 2012 |
| Estimation and Inference on nonlinear and heterogeneous effects M Ratkovic, D Tingley The Journal of Politics 85 (2), 421-435, 2023 | 6 | 2023 |
| Rehabilitating the regression: Honest and valid causal inference through machine learning M Ratkovic | 5 | 2019 |
| Politics, Institutions, and Trade IS Kim, J Londregan, M Ratkovic Working Paper, 2016 | 5 | 2016 |
| Strengthening weak instruments by modeling compliance M Ratkovic, Y Shiraito annual meeting of the Midwest Political Science Association, 2014 | 5 | 2014 |
