Donald B. Rubin

About

Donald B. Rubin is an Emeritus Professor of Statistics at Harvard University. His research interests include causal inference in experiments and observational studies, inference in sample surveys with nonresponse and in missing data problems, and the application of Bayesian and empirical Bayesian techniques. Rubin has developed and applied statistical models across various scientific disciplines. He earned his Ph.D. in Statistics from Harvard University in 1970, his M.A. in Computer Science from Harvard in 1966, and his A.B. in Psychology from Princeton University in 1965. Rubin served as a Professor in the Department of Statistics at Harvard from 1984 until his retirement in 2018, and he held the position of Chairman of the department during two separate periods. His professional experience also includes being a Fellow in Theoretical and Applied Statistics at the National Bureau of Economic Research and a Research Associate at NORC in Chicago.

Research topics

• Computer Science
• Political Science
• Data Mining
• Computer Security
• Mathematical analysis
• World Wide Web
• Mathematics
• Applied mathematics
• Data science
• Medicine

Selected publications

• Automatic detection of influential actors in disinformation networks

  Proceedings of the National Academy of Sciences · 2021 · 61 citations

  Senior authorCorresponding

  The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a network causal inference approach to quantify the impact of…

  PublisherOA PDFDOI

• Estimating adjusted risk differences by multiply‐imputing missing control binary potential outcomes following propensity score‐matching

  Statistics in Medicine · 2021-08-10 · 3 citations

  articleOpen access

  We describe a new method to combine propensity-score matching with regression adjustment in treatment-control studies when outcomes are binary by multiply imputing potential outcomes under control for the matched treated subjects. This enables the estimation of clinically meaningful measures of effect such as the risk difference. We used Monte Carlo simulation to explore the effect of the number of imputed potential outcomes under control for the matched treated subjects on inferences about the…

  PublisherOA PDFDOI

• PCA Rerandomization

  Canadian Journal of Statistics · 2023-02-16 · 2 citations

  preprintOpen accessSenior author

  Abstract Mahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing a rerandomization strategy. However, this criterion may not work well for high‐dimensional cases because it balances all orthogonalized covariates equally. We propose using principal component analysis (PCA) to identify proper subspaces in which Mahalanobis distance should be calculated. Not only can PCA effectively reduce the dimensionality for high‐dim…

  PublisherOA PDFDOI

• Catalytic Priors: Using Synthetic Data to Specify Prior Distributions in Bayesian Analysis

  arXiv (Cornell University) · 2022-08-30 · 2 citations

  preprintOpen access

  Catalytic prior distributions provide general, easy-to-use, and interpretable specifications of prior distributions for Bayesian analysis. They are particularly beneficial when the observed data are inadequate to stably estimate a complex target model. A catalytic prior distribution is constructed by augmenting the observed data with synthetic data that are sampled from the predictive distribution of a simpler model estimated from the observed data. We illustrate the usefulness of the catalytic…

  PublisherOA PDFDOI

• Contrast-specific propensity scores

  Biostatistics & Epidemiology · 2021-01-02 · 1 citations

  preprintOpen accessSenior author

  Basic propensity score methodology is designed to balance the distributions of multivariate pre-treatment covariates when comparing one active treatment with one control treatment. However, practical settings often involve comparing more than two treatments, where more complicated contrasts than the basic treatment-control one, (1,−1), are relevant. Here, we propose the use of contrast-specific propensity scores (CSPS), which allows the creation of treatment groups of units that are balanced wit…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Generalized Propensity Score Methods

  NSF · $135k · 2006–2010

Frequent coauthors

• Guido W. Imbens

  Stanford University

  79 shared

• Robert Rosenthal

  57 shared

• Constantine Frangakis

  39 shared

• John Barnard

  32 shared

• Roderick J. A. Little

  University of Michigan–Ann Arbor

  31 shared

• Jennifer Hill

  New York University

  28 shared

• Xiao‐Hua Zhou

  Peking University International Hospital

  25 shared

• Martin McIntosh

  25 shared

Labs

• Sports Analytics Laboratory at Harvard UniversityPI

  The Sports Analytics Laboratory at Harvard University focuses on the application of statistical methods to sports data.

Education

• B.A., Mathematics

  Harvard University

  1963

• M.A., Statistics

  Harvard University

  1965

• Ph.D., Statistics

  Harvard University

  1968

Similar researchers at Harvard University

• Susan Murphyh-188
• Giovanni Parmigianih-141
• Kosuke Imaih-67