| Maximum Likelihood from Incomplete Data Via the EM Algorithm AP Dempster, NM Laird, DB Rubin Journal of the royal statistical society: series B (methodological) 39 (1), 1-22, 1977 | 75519 | 1977 |
| The central role of the propensity score in observational studies for causal effects PR Rosenbaum, DB Rubin Biometrika 70 (1), 41-55, 1983 | 40915 | 1983 |
| Bayesian data analysis A Gelman, J Carlin, H Stern, DB Rubin CRC press, 2004 | 40907* | 2004 |
| Statistical analysis with missing data RJA Little, DB Rubin John Wiley & Sons, 2019 | 38251 | 2019 |
| Multiple imputation DB Rubin Flexible imputation of missing data, second edition, 29-62, 2018 | 30522 | 2018 |
| Inference from iterative simulation using multiple sequences A Gelman, DB Rubin Statistical science 7 (4), 457-472, 1992 | 19894 | 1992 |
| Inference and missing data DB Rubin Biometrika 63 (3), 581-592, 1976 | 14419 | 1976 |
| Estimating causal effects of treatments in randomized and nonrandomized studies. DB Rubin Journal of educational Psychology 66 (5), 688, 1974 | 12903 | 1974 |
| Constructing a control group using multivariate matched sampling methods that incorporate the propensity score PR Rosenbaum, DB Rubin The American Statistician 39 (1), 33-38, 1985 | 8412 | 1985 |
| Identification of causal effects using instrumental variables JD Angrist, GW Imbens, DB Rubin Journal of the American statistical Association 91 (434), 444-455, 1996 | 8240 | 1996 |
| Causal inference in statistics, social, and biomedical sciences GW Imbens, DB Rubin Cambridge university press, 2015 | 6842 | 2015 |
| Reducing bias in observational studies using subclassification on the propensity score PR Rosenbaum, DB Rubin Journal of the American statistical Association 79 (387), 516-524, 1984 | 5451 | 1984 |
| Multiple imputation after 18+ years DB Rubin Journal of the American statistical Association 91 (434), 473-489, 1996 | 5303 | 1996 |
| Testing the number of components in a normal mixture Y Lo, NR Mendell, DB Rubin Biometrika 88 (3), 767-778, 2001 | 5298 | 2001 |
| Randomization analysis of experimental data: The Fisher randomization test comment DB Rubin Journal of the American statistical association 75 (371), 591-593, 1980 | 3592* | 1980 |
| Bayesian inference for causal effects: The role of randomization DB Rubin The Annals of statistics, 34-58, 1978 | 3568 | 1978 |
| Estimating causal effects from large data sets using propensity scores DB Rubin Annals of internal medicine 127 (8_Part_2), 757-763, 1997 | 3456 | 1997 |
| Comparing correlated correlation coefficients. XL Meng, R Rosenthal, DB Rubin Psychological bulletin 111 (1), 172, 1992 | 3045 | 1992 |
| Causal inference using potential outcomes: Design, modeling, decisions DB Rubin Journal of the American statistical Association 100 (469), 322-331, 2005 | 3003 | 2005 |
| Using propensity scores to help design observational studies: application to the tobacco litigation DB Rubin Health Services and Outcomes Research Methodology 2, 169-188, 2001 | 2914 | 2001 |