A conversation with Donald B. Rubin

Donald Bruce Rubin is John L. Loeb Professor of Statistics at Harvard University. He has made fundamental contributions to statistical methods for missing data, causal inference, survey sampling, Bayesian inference, computing and applications to a wide range of disciplines, including psychology, education, policy, law, economics, epidemiology, public health and other social and biomedical sciences.

[1]  P. Holland.  Statistics and Causal Inference , 1985 .

[2]  R. R. Hocking,et al.  The analysis of incomplete data. , 1971 .

[3]  Donald B. Rubin,et al.  Selection modelling versus mixture modelling with nonignorable nonresponse , 1986 .

[4]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[5]  Donald B. Rubin,et al.  A Non‐Iterative Algorithm for Least Squares Estimation of Missing Values in Any Analysis of Variance Design , 1972 .

[6]  D. Rubin,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[7]  Lena Osterhagen,et al.  Multiple Imputation For Nonresponse In Surveys , 2016 .

[8]  Donald B. Rubin,et al.  Multiple Imputation by Ordered Monotone Blocks With Application to the Anthrax Vaccine Research Program , 2014 .

[9]  D. Rubin.  The ethics of consulting for the tobacco industry , 2002, Statistical methods in medical research.

[10]  Donald B. Rubin,et al.  Comment : A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest : The SIR Algorithm , 1987 .

[11]  Alessandra Mattei,et al.  Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program , 2013, 1401.2344.

[12]  Donald B. Rubin,et al.  Comment : Neyman ( 1923 ) and Causal Inference in Experiments and Observational Studies , 2007 .

[13]  Donald B Rubin,et al.  Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010). , 2010, Psychological methods.

[14]  Donald B. Rubin,et al.  Sensitivity of Bayes Inference with Data-Dependent Stopping Rules , 1984 .

[15]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[16]  Raul Cano.  On The Bayesian Bootstrap , 1992 .

[17]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[18]  A. Brix.  Bayesian Data Analysis, 2nd edn , 2005 .

[19]  D. Rubin.  Multiple Imputation After 18+ Years , 1996 .

[20]  J. Neyman.  On the Two Different Aspects of the Representative Method: the Method of Stratified Sampling and the Method of Purposive Selection , 1934 .

[21]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[22]  Xiao-Li Meng,et al.  POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES , 1996 .

[23]  D. Rubin,et al.  Identification of Causal Effects Using Instrumental Variables: Rejoinder , 1996 .

[24]  Russell V. Lenth,et al.  Statistical Analysis With Missing Data (2nd ed.) (Book) , 2004 .

[25]  D. Rubin,et al.  Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction , 2016 .

[26]  Donald B. Rubin,et al.  Formal modes of statistical inference for causal effects , 1990 .

[27]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[28]  H. O. Hartley,et al.  A Plan for Programming Analysis of Variance for General Purpose Computers , 1956 .

[29]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[30]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[31]  Donald B. Rubin,et al.  A Case Study of the Robustness of Bayesian Methods of Inference: Estimating the Total in a Finite Population Using Transformations to Normality , 1983 .

[32]  Donald B. Rubin.  BAYES, NEYMAN, AND CALIBRATION , 1995 .

[33]  D. Rubin.  Converting rejections into positive stimuli , 2014 .

[34]  The management of weather resources. , 1978, Science.

[35]  Xiao-Li Meng,et al.  Posterior Predictive $p$-Values , 1994 .

[36]  D. Rubin,et al.  Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome , 1983 .

[37]  Y. Liu,et al.  AN ANALYSIS OF , 2008 .

[38]  D. Rubin.  Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician , 1984 .

[39]  J. Angrist,et al.  Identification and Estimation of Local Average Treatment Effects , 1995 .

[40]  George E. P. Box,et al.  Sampling and Bayes' inference in scientific modelling and robustness , 1980 .

[41]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[42]  D. Rubin,et al.  Bayesian inference for causal effects in randomized experiments with noncompliance , 1997 .

[43]  D. Rubin.  [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .

[44]  Donald B. Rubin,et al.  Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data , 2012 .

[45]  D. Rubin.  ASSIGNMENT TO TREATMENT GROUP ON THE BASIS OF A COVARIATE , 1976 .

[46]  J. Angrist,et al.  Identification and Estimation of Local Average Treatment Effects , 1994 .

[47]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[48]  Fabrizia Mealli,et al.  Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance , 2013 .

[49]  T. Speed,et al.  On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .

[50]  D. Rubin,et al.  Principal Stratification in Causal Inference , 2002, Biometrics.

[51]  D. Rubin,et al.  MULTIPLE IMPUTATIONS IN SAMPLE SURVEYS-A PHENOMENOLOGICAL BAYESIAN APPROACH TO NONRESPONSE , 2002 .

[52]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[53]  A. Margulis.  The Importance of Mentors , 2011 .

[54]  Joshua D. Angrist,et al.  Identification of Causal Effects Using Instrumental Variables , 1993 .