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Donald B Rubin
Donald B Rubin
YCMS Tsinghua; Senior Research Fellow at Fox / Temple U, Emeritus at Harvard U
Verified email at temple.edu
Title
Cited by
Cited by
Year
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
721721977
The central role of the propensity score in observational studies for causal effects
PR Rosenbaum, DB Rubin
Biometrika 70 (1), 41-55, 1983
377741983
Bayesian data analysis
A Gelman, J Carlin, H Stern, DB Rubin
CRC press, 2004
37731*2004
Statistical analysis with missing data
RJA Little, DB Rubin
John Wiley & Sons, 2019
361392019
Multiple imputation
DB Rubin
Flexible Imputation of Missing Data, Second Edition, 29-62, 2018
281912018
Inference from iterative simulation using multiple sequences
A Gelman, DB Rubin
Statistical science 7 (4), 457-472, 1992
180741992
Inference and missing data
DB Rubin
Biometrika 63 (3), 581-592, 1976
134961976
Estimating causal effects of treatments in randomized and nonrandomized studies.
DB Rubin
Journal of educational Psychology 66 (5), 688, 1974
117191974
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
78971985
Identification of causal effects using instrumental variables
JD Angrist, GW Imbens, DB Rubin
Journal of the American statistical Association 91 (434), 444-455, 1996
76761996
Causal inference in statistics, social, and biomedical sciences
GW Imbens, DB Rubin
Cambridge University Press, 2015
57082015
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
51831984
Multiple imputation after 18+ years
DB Rubin
Journal of the American statistical Association 91 (434), 473-489, 1996
50541996
Testing the number of components in a normal mixture
Y Lo, NR Mendell, DB Rubin
Biometrika 88 (3), 767-778, 2001
47642001
Bayesian inference for causal effects: The role of randomization
DB Rubin
The Annals of statistics, 34-58, 1978
33841978
Estimating causal effects from large data sets using propensity scores
DB Rubin
Annals of internal medicine 127 (8_Part_2), 757-763, 1997
33761997
Randomization analysis of experimental data: The Fisher randomization test comment
DB Rubin
Journal of the American statistical association 75 (371), 591-593, 1980
3200*1980
Comparing correlated correlation coefficients.
XL Meng, R Rosenthal, DB Rubin
Psychological bulletin 111 (1), 172, 1992
29221992
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
25962001
Causal inference using potential outcomes: Design, modeling, decisions
DB Rubin
Journal of the American Statistical Association 100 (469), 322-331, 2005
25502005
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