Donald B. Rubin

Profile Picture of Donald B. Rubin

Donald B. Rubin

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Research Professor

      • Shusterman Senior Research Fellow


Donald B. Rubin is John L. Loeb Professor of Statistics, Harvard University, where he has been professor since 1983, and Department Chair for 13 of those years. He has been elected to be a Fellow/Member/Honorary Member of: the Woodrow Wilson Society, Guggenheim Memorial Foundation, Alexander von Humboldt Foundation, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute, American Association for the Advancement of Science, American Academy of Arts and Sciences, European Association of Methodology, the British Academy, and the U.S. National Academy of Sciences

As of 2017, he has authored/coauthored over 400 publications (including ten books), has four joint patents, and for many years has been one of the most highly cited authors in the world, with currently over 200,000 citations and nearly 20,000 in 2016 alone (Google Scholar). He has received honorary doctorate degrees from Otto Friedrich University, Bamberg, Germany; University of Ljubljana, Slovenia; Universidad Santo Tomás, Bogotá, Colombia; Uppsala University, Sweden; and Northwestern University, Evanston, Illinois.

He has also received honorary professorships from University of Utrecht, The Netherlands; Shanghai Finance University, China; Nanjing University of Science & Technology, China; Xi’an University of Technology, China; and University of the Free State, Republic of South Africa.

Research Interests

  • Causal inference in experiments and observational studies
  • Inference in sample surveys with nonresponse and in missing data problems
  • Application of Bayesian and empirical Bayesian techniques
  • Developing and applying statistical models to data in a variety of scientific disciplines

Selected Publications


  • Bind, M., Rubin, D., Cardenas, A., Dhingra, R., Ward-Caviness, C., Liu, Z., Mirowsky, J., Schwartz, J., Diaz-Sanchez, D., & Devlin, R. (2020). Heterogeneous ozone effects on the DNA methylome of bronchial cells observed in a crossover study. Sci Rep, 10(1), 15739. England. 10.1038/s41598-020-72068-6

  • Franks, A., Airoldi, E., & Rubin, D. (2020). Nonstandard conditionally specified models for nonignorable missing data. Proceedings of the National Academy of Sciences of the United States of America, 117(32), 19045-19053. doi: 10.1073/pnas.1815563117.

  • Efron, B., Amari, S., Rubin, D.B., Rao, A.S.S., & Cox, D.R. (2020). C. R. Rao's century. Significance, 17(4), 36-38. doi: 10.1111/1740-9713.01424.

  • Huang, D., Stein, N., Rubin, D.B., & Kou, S. (2020). Catalytic prior distributions with application to generalized linear models. Proc Natl Acad Sci U S A, 117(22), 12004-12010. United States. 10.1073/pnas.1920913117

  • Bojinov, I., Pillai, N., & Rubin, D. (2020). Diagnosing missing always at random in multivariate data. Biometrika, 107(1), 246-253. doi: 10.1093/biomet/asz061.

  • Austin, P.C., Thomas, N., & Rubin, D.B. (2020). Covariate-adjusted survival analyses in propensity-score matched samples: Imputing potential time-to-event outcomes. Stat Methods Med Res, 29(3), 728-751. England. 10.1177/0962280218817926

  • Li, X., Ding, P., & Rubin, D. (2020). Rerandomization in 2K factorial experiments. Annals of Statistics, 48(1), 43-63. doi: 10.1214/18-AOS1790.

  • Rubin, D.B. (2019). The practical importance of understanding placebo effects and their role when approving drugs and recommending doses for medical practice. Behaviormetrika, 47(1), 5-18. doi: 10.1007/s41237-019-00091-7.

  • Bind, M.C. & Rubin, D.B. (2019). Bridging observational studies and randomized experiments by embedding the former in the latter. Statistical Methods in Medical Research, 28(7), 1958-1978. SAGE Publications. doi: 10.1177/0962280217740609.

  • Rubin, D.B. (2019). Conditional calibration and the sage statistician. SURVEY METHODOLOGY, 45(2), 187-198. Retrieved from

  • Rubin, D. (2019). Essential concepts of causal inference: a remarkable history and an intriguing future. Biostatistics and Epidemiology, 3(1), 140-155. doi: 10.1080/24709360.2019.1670513.