D. Rubin

发表

Maximum likelihood estimation via the ECM algorithm: A general framework

Xiao-Li Meng, D. Rubin, X. Meng, 1993 .

Estimating causal effects of treatments in randomized and nonrandomized studies.

D. Rubin, 1974 .

The central role of the propensity score in observational studies for causal effects

D. Rubin, P. Rosenbaum, Donald B. Rubin, 1983 .

[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

D. Rubin, 1990 .

Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

D. Rubin, P. Rosenbaum, 1984 .

EM algorithms for ML factor analysis

Dorothy T. Thayer, D. Rubin, 1982 .

Statistical Analysis with Missing Data.

D. Rubin, R. Little, Martin G. Gibson, 1989 .

On Jointly Estimating Parameters and Missing Data by Maximizing the Complete-Data Likelihood

D. Rubin, R. Little, 1983 .

Max-imum Likelihood from Incomplete Data

Donald B. Rubin, Arthur P. Dempster, Nan M. Laird, 1972 .

Parameter expansion to accelerate EM: The PX-EM algorithm

D. Rubin, Chuanhai Liu, Y. Wu, 1998 .

The calculation of posterior distributions by data augmentation

D. Rubin, W. Wong, M. Tanner, 1987 .

ML ESTIMATION OF THE t DISTRIBUTION USING EM AND ITS EXTENSIONS, ECM AND ECME

D. Rubin, Chuanhai Liu, 1999 .

INFERENCE AND MISSING DATA

D. Rubin, 1975 .

Causal Inference Using Potential Outcomes

D. Rubin, 2005 .

Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment

D. Rubin, 1980 .

On the global and componentwise rates of convergence of the EM algorithm

Xiao-Li Meng, D. Rubin, X. Meng, 1994 .

The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence

D. Rubin, Chuanhai Liu, 1994 .

Bayesian Data Analysis

David B. Dunson, Aki Vehtari, Hal S. Stern, 2010 .

Multiple Imputation for Nonresponse in Surveys

D. Rubin, W. M. Campion, 1989 .

Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups?

D. Rubin, 2009 .

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

D. Rubin, G. Imbens, C. Blumberg, 2016 .

Xiao-Li Meng, D. Rubin, R. Rosenthal, 1992 .

The Bayesian Bootstrap

D. Rubin, 1981 .

Multiple Imputation for Statistical Disclosure Limitation

Jerome P. Reiter, D. Rubin, T. Raghunathan, 2003 .

Causal Inference for Statistics, Social, and Biomedical Sciences: Causality: The Basic Framework

D. Rubin, G. Imbens, 2015 .

Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies

D. Rubin, 1973 .

Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome

D. Rubin, P. Rosenbaum, 1983 .

Discussion of: 'Causal Diagrams for Empirical Research' by J. Pearl

D. Rubin, G. Imbens, 1995 .

Multiple Imputation After 18+ Years

D. Rubin, 1996 .

Fully conditional specification in multivariate imputation

D. Rubin, S. van Buuren, J. Brand, 2006 .

Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse

D. Rubin, N. Schenker, 1986 .

Multiple imputation for nonresponse in surveys

D. Rubin, 1989 .

Estimation in Covariance Components Models

D. Rubin, A. Dempster, R. Tsutakawa, 1981 .

Rerandomization to improve covariate balance in experiments

Kari Lock Morgan, D. Rubin, 2012, 1207.5625.

Statistics and Causal Inference: Comment: Which Ifs Have Causal Answers

D. Rubin, 1986 .

Comment: Which Ifs Have Causal Answers

D. Rubin, 1986 .

Causal Inference in Retrospective Studies

D. Rubin, P. Holland, 1987 .

Formalizing Subjective Notions about the Effect of Nonrespondents in Sample Surveys

D. Rubin, 1977 .

ON LORD'S PARADOX

D. Rubin, P. Holland, 1982 .

Testing the number of components in a normal mixture

D. Rubin, N. Mendell, Y. Lo, 2001 .

Estimation and Hypothesis Testing in Finite Mixture Models

D. Rubin, M. Aitkin, 1985 .

Causal Inference for Statistics, Social, and Biomedical Sciences: Sensitivity Analysis and Bounds

D. Rubin, G. Imbens, 2015 .

D. Rubin, R. Rosenthal, T. Raghunathan, 1996 .

Imputing Missing Data by Fully Conditional Models : Some Cautionary Examples and Guidelines

Yaming Yu, D. Rubin, Fan Li, 2012 .

Causal Inference for Statistics, Social, and Biomedical Sciences: A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects

D. Rubin, G. Imbens, 2015 .

A method for calibrating false-match rates in record linkage

D. Rubin, T. Belin, 1995 .

The Bias Due to Incomplete Matching

D. Rubin, P. Rosenbaum, 1985 .

Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score

D. Rubin, P. Rosenbaum, 1985 .

Controlling Bias in Observational Studies: A Review.

W. G. Cochran, D. Rubin, 1974 .

In utero exposure to phenobarbital and intelligence deficits in adult men.

D. Rubin, J. Reinisch, S. Sanders, 1995, JAMA.

Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies

D. Rubin, Donald B. Rubin, 1978 .

For objective causal inference, design trumps analysis

D. Rubin, 2008, 0811.1640.

A Research Agenda for Assessment and Propagation of Model Uncertainty

D. Rubin, C. Morris, J. Hodges, 1987 .

Ignorability and Coarse Data

D. Rubin, D. Heitjan, 1991 .

Bayesian data analysis, third edition

David B. Dunson, Aki Vehtari, Hal S. Stern, 2013 .

MULTIPLE IMPUTATIONS IN SAMPLE SURVEYS-A PHENOMENOLOGICAL BAYESIAN APPROACH TO NONRESPONSE

D. Rubin, Donald B. Rubin, 2002 .

Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach

D. Rubin, R. Rosenthal, R. L. Rosnow, 1999 .

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

Donald B. Rubin, D. Rubin, 1987 .

Statistical Analysis with Missing Data

Roderick J. A. Little, Donald B. Rubin, Martin G. Gibson, 1988 .

Comment on "Causal inference without counterfactuals," by Dawid AP

D. Rubin, 2000 .

Estimating Outcome Distributions for Compliers in Instrumental Variables Models

D. Rubin, G. Imbens, 1997 .

Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation

D. Rubin, 2001, Health Services and Outcomes Research Methodology.

Missing data, imputation, and the bootstrap. Comment

D. Rubin, B. Efron, 1994 .

Interpersonal expectancy effects: the first 345 studies

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1978, Behavioral and Brain Sciences.

The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles.

Donald B. Rubin, Nageswari Rajaratnam, Lee J. Cronbach, 1974 .

Further meta-analytic procedures for assessing cognitive gender differences.

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1982 .

A Simple, General Purpose Display of Magnitude of Experimental Effect

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1982 .

Comparing Effect Sizes of Independent Studies

D. Rubin, R. Rosenthal, 1982 .

Contrasts and Correlations in Effect-Size Estimation

D. Rubin, R. Rosenthal, R. L. Rosnow, 2000, Psychological science.

BIAS REDUCTION USING MAHALANOBIS METRIC MATCHING

D. Rubin, 1978 .

Converting rejections into positive stimuli

D. Rubin, 2014 .

EM and beyond

Donald B. Rubin, D. Rubin, 1991 .

More on EM for ML factor analysis

Donald B. Rubin, Dorothy T. Thayer, D. Rubin, 1983 .

Difficulties with regression analyses of age-adjusted rates.

D B Rubin, D. Rubin, P. Rosenbaum, 1984, Biometrics.

Rumor and Gossip Research

Donald B. Rubin, Robert Rosenthal, Eric K. Foster, 2005 .

The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials

D. Rubin, 2007, Statistics in medicine.

r equivalent: A simple effect size indicator.

D. Rubin, R. Rosenthal, 2003, Psychological methods.

Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments

D. Rubin, R. Mukerjee, Tirthankar Dasgupta, 2018 .

Causal Inference in Rebuilding and Extending the Recondite Bridge between Finite Population Sampling and Experimental Design

D. Rubin, R. Mukerjee, Tirthankar Dasgupta, 2016, 1606.05279.

Improving Covariate Balance in 2^K Factorial Designs via Rerandomization

D. Rubin, Tirthankar Dasgupta, Zach Branson, 2015, 1511.01973.

Causal inference from $2^k$ factorial designs using the potential outcomes model

D. Rubin, N. Pillai, Tirthankar Dasgupta, 2012, 1211.2481.

Assignment to Treatment Group on the Basis of a Covariate

D. Rubin, 1976 .

2 Statistical Inference for Causal Effects, With Emphasis on Applications in Epidemiology and Medical Statistics

D. Rubin, 2007 .

24 Statistical Inference for Causal Effects, with Emphasis on Applications in Psychometrics and Education

D. Rubin, 2006 .

Assumptions allowing the estimation of direct causal effects

D. Rubin, F. Mealli, 2003 .

Comment: Estimating the Effects Caused by Treatments

D. Rubin, P. Rosenbaum, 1984 .

On the Nature and Discovery of Structure: Comment

D. Rubin, P. Rosenbaum, 1984 .

Causal Inference in Retrospective Studies

D. Rubin, P. Holland, 1987 .

The bias due to incomplete matching.

D. Rubin, P. Rosenbaum, 1983, Biometrics.

Comments on the Neyman-Fisher Controversy and Its Consequences

D. Rubin, Arman Sabbaghi, 2014, 1409.2675.

Large-sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution

D. Rubin, T. Raghunathan, K. H. Li, 1991 .

Estimation in Parallel Randomized Experiments

D. Rubin, 1981 .

Principal Stratification in Causal Inference

D. Rubin, C. Frangakis, 2002, Biometrics.

Small-sample degrees of freedom with multiple imputation

D. Rubin, J. Barnard, 1999 .

Direct and Indirect Causal Effects via Potential Outcomes *

D. Rubin, 2004 .

Causal Effects of Perceived Immutable Characteristics

D. Rubin, D. Greiner, 2011, Review of Economics and Statistics.

Outcome-free Design of Observational Studies: Peer Influence on Smoking

D. Rubin, S. Langenskiöld, Sophie Langenskiöld, 2008 .

The Varying Role of Voter Information Across Democratic Societies

Henry E. Brady, Robert H. Durr, D. Rubin, 2007 .

Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with “Censoring” Due to Death

D. Rubin, 2006, math/0612783.

Estimating the Effects Caused by Treatments

D. Rubin, P. Rosenbaum, 1984 .

Assessing Sensitivity to an Unobserved Binary Covariate in a Nonrandomized Experiment with Binary Outcome.

D. Rubin, P. Rosenbaum, 1982 .

ASSIGNMENT TO TREATMENT GROUP ON THE BASIS OF A COVARIATE

D. Rubin, 1976 .

Assessing the effect of an influenza vaccine in an encouragement design.

D. Rubin, G. Imbens, K. Hirano, 2000, Biostatistics.

Identification of Causal Effects Using Instrumental Variables: Rejoinder

D. Rubin, J. Angrist, G. Imbens, 1996 .

Contrasts and Effect Sizes in Behavioral Research

D. Rubin, R. Rosenthal, R. L. Rosnow, 1999 .

Bias Reduction Using Mahalanobis-Metric Matching

D. Rubin, 1980 .

Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates

D. Rubin, N. Thomas, Neal Thomas, 2000 .

Discussion on Multiple Imputation

D. Rubin, 2003 .

Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes

D. Rubin, C. Frangakis, 1999 .

Rounding Error in Regression: The Appropriateness of Sheppard's Corrections,

Donald B. Rubin, Arthur P. Dempster, D. Rubin, 1983 .

Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Death”

Donald B. Rubin, D. Rubin, Junni L. Zhang, 2003 .

Matching with Multiple Control Groups , and Adjusting for Group Differences

Donald B. Rubin, D. Rubin, 2005 .

Matching With Multiple Control Groups With Adjustment for Group Differences

Donald B. Rubin, Elizabeth A. Stuart, D. Rubin, 2008 .

Rerandomization to Balance Tiers of Covariates

Donald B Rubin, Kari Lock Morgan, D. Rubin, 2015, Journal of the American Statistical Association.

Asymptotic theory of rerandomization in treatment–control experiments

Donald B. Rubin, Peng Ding, Xinran Li, 2016, Proceedings of the National Academy of Sciences.

AN OVERVIEW OF MULTIPLE IMPUTATION

Donald B. Rubin, D. Rubin, 2002 .

Multiple Imputation of Industry and Occupation Codes in Census Public-use Samples Using Bayesian Logistic Regression

Donald B. Rubin, Nathaniel Schenker, Clifford C. Clogg, 1991 .

Significance levels from repeated p-values with multiply imputed data

Donald B. Rubin, Xiao-Li Meng, Xiao-Li Meng, 1991 .

Performing likelihood ratio tests with multiply-imputed data sets

Donald B. Rubin, Xiao-Li Meng, Xiao-Li Meng, 1992 .

Meta-Analytic Procedures for Combining Studies With Multiple Effect Sizes

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1986 .

Comparing Significance Levels of Independent Studies

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1979 .

Comment: Dose-Response Estimands

Donald B. Rubin, D. Rubin, 1991 .

Intention‐to‐treat analysis and the goals of clinical trials

D. Rubin, L. Sheiner, Donald B. Rubin, 1995, Clinical pharmacology and therapeutics.

MULTIPLE IMPUTATION OF MISSING BLOOD ALCOHOL CONCENTRATION (BAC) VALUES IN FARS

D. Rubin, J. Schafer, R. Subramanian, 1998 .

Sequential rerandomization

Anru Zhang, Philip A. Ernst, Donald B Rubin, 2017, Biometrika.

Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models: Comment

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 1999 .

Issues in summarizing the first 345 studies of interpersonal expectancy effects

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1978, Behavioral and Brain Sciences.

Using Empirical Bayes Techniques in the Law School Validity Studies

Donald B. Rubin, D. Rubin, 1980 .

Reliability of measurement in psychology: From Spearman-Brown to maximal reliability.

Donald B. Rubin, Heng Li, Robert Rosenthal, 1996 .

The practical importance of understanding placebo effects and their role when approving drugs and recommending doses for medical practice

Donald B. Rubin, D. Rubin, 2019, Behaviormetrika.

Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

D. Rubin, N. Laird, A. Dempster, 1977 .

Statistical Inference for Causal Effects in Clinical Psychology

D. Rubin, J. Zubizarreta, Reagan Mozer, 2020 .

Credible Causal Inference for Empirical Legal Studies

Daniel E. Ho, D. Rubin, 2011 .

Rejection, rebuttal, revision: Some flexible features of peer review

D. Rubin, 1982, Behavioral and Brain Sciences.

Inference from Coarse Data via Multiple Imputation with Application to Age Heaping

Donald B. Rubin, Daniel F. Heitjan, D. Rubin, 1990 .

The Computerized Construction of a Matched Sample

Donald B. Rubin, Robert P. Althauser, D. Rubin, 1970, American Journal of Sociology.

Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies

D. Rubin, 1973 .

Robust estimation of causal effects of binary treatments in unconfounded studies with dichotomous outcomes

D B Rubin, R Gutman, D. Rubin, 2013, Statistics in medicine.

Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010).

Donald B Rubin, D. Rubin, 2010, Psychological methods.

A Potential Outcomes View of Value-Added Assessment in Education

Donald B. Rubin, Elizabeth A. Stuart, Elaine L. Zanutto, 2004 .

The ethics of consulting for the tobacco industry

D. Rubin, 2002, Statistical methods in medical research.

On estimating the causal effects of DNR orders.

Donald B. Rubin, D. Rubin, M. McIntosh, 1999, Medical Care.

Multivariate matching methods that are equal percent bias reducing

Donald B. Rubin, D. Rubin, 1974 .

MULTIVARIATE MATCHING METHODS THAT ARE EQUAL PERCENT BIAS REDUCING, I: SOME EXAMPLES

Donald B. Rubin, D. Rubin, 1974 .

Causality Regularity Models of Causation

D. Rubin, J. Neyman, David Lewis, 2022 .

Principal Stratification for Causal Inference With Extended Partial Compliance

Donald B Rubin, D. Rubin, Hui Jin, 2008 .

Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling.

D. Rubin, G. McLachlan, M. Lenzenweger, 2007, Journal of abnormal psychology.

Clustered encouragement designs with individual noncompliance: bayesian inference with randomization, and application to advance directive forms.

Xiao-Hua Zhou, Constantine E Frangakis, Donald B Rubin, 2002, Biostatistics.

Nonstandard conditionally specified models for nonignorable missing data

D. Rubin, E. Airoldi, Alexander M. Franks, 2016, Proceedings of the National Academy of Sciences.

A direct likelihood approach to principal stratification analysis

D. Rubin, P. Frumento, F. Mealli, 2013 .

Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification

Donald B. Rubin, Fabrizia Mealli, D. Rubin, 2009 .

Evaluating the e ff ect of training on wages in the presence of noncompliance

D. Rubin, 2009 .

Principal Stratification Approach to Broken Randomized Experiments

Donald B. Rubin, John Barnard, Jennifer Hill, 2003 .

Principal Strati cation Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City

D. Rubin, Jennifer L. Hill, C. Frangakis, 2003 .

Discussion

D. Rubin, 2008 .

Catalytic prior distributions with application to generalized linear models

D. Rubin, S. Kou, Dongming Huang, 2020, Proceedings of the National Academy of Sciences.

Valid randomization‐based p‐values for partially post hoc subgroup analyses

D. Rubin, Joseph J Lee, 2015, Statistics in medicine.

Estimation of causal effects of binary treatments in unconfounded studies

Roee Gutman, Donald B Rubin, D. Rubin, 2015, Statistics in medicine.

Inference from Iterative Simulation Using Multiple Sequences

D. Rubin, A. Gelman, 1992 .

Dealing with noncompliance and missing outcomes in a randomized trial using Bayesian technology: Prevention of perinatal sepsis clinical trial, Soweto, South Africa

Donald B. Rubin, Elizabeth R. Zell, D. Rubin, 2010 .

Bayesian Inference for Causal Effects: The Role of Randomization

Donald B. Rubin, D. Rubin, 1978 .

A Bayesian Perspective on the Analysis of Unreplicated Factorial Designs Using Potential Outcomes

D. Rubin, Tirthankar Dasgupta, Valeria Espinosa, 2014 .

Bugs, Lacunae, and the Minnesota/DC Effect—A Discussion of H. Wainer’s “Eelworms, Bullet Holes, and Geraldine Ferraro”

D. Rubin, 1989 .

INFERENCE AND MISSING DATA

D. Rubin, 1975 .

SELECTING THE “BEST” REGRESSION WHEN FACED WITH MISSING OBSERVATIONS

D. Rubin, 1975 .

A NOTE ON A SIMPLE PROBLEM IN INFERENCE

D. Rubin, 1975 .

Meta-Analysis: Literature Synthesis or Effect-Size Surface Estimation?

D. Rubin, 1992 .

Multivariate matching methods that are equal percent bias reducing

Donald B. Rubin, D. Rubin, 1974 .

COMPARING HIGH SCHOOLS WITH RESPECT TO STUDENT PERFORMANCE IN UNIVERSITY

D. Rubin, T. Stroud, 1977 .

Comparing Regressions When Some Predictor Values Are Missing

Donald B. Rubin, D. Rubin, 1976 .

Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician

D. Rubin, 1984 .

Comment

D. Rubin, 2000 .

Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm

Xiao-Li Meng, D. Rubin, X. Meng, 1991 .

Rerandomization in $2^{K}$ factorial experiments

D. Rubin, P. Ding, Xinran Li, 2018, The Annals of Statistics.

Multiple-bias modelling for analysis of observational data - Discussion

David J. Spiegelhalter, Donald B. Rubin, Andrew E. Gelman, 2005 .

SOME APPLICATIONS OF MULTILEVEL MODELS TO EDUCATIONAL DATA11This paper is a modification of “Some applications of Bayesian models to educational data,” which appeared in The Statistician, 32, 1983.

Donald B. Rubin, D. Rubin, 1989 .

Finding the "genuine" schizotype: a model and method for resolving heterogeneity in performance on laboratory measures in experimental psychopathology research.

D. Rubin, M. Lenzenweger, S. Jensen, 2003, Journal of abnormal psychology.

A Bayesian Approach to Reducing Heterogeneity in Laboratory Measures: An Illustration from Schizophrenia Research

D. Rubin, M. Lenzenweger, S. Jensen, 2002 .

Multiply-Imputed Synthetic Data: Advice to the Imputer

D. Rubin, B. Loong, Bronwyn Loong, 2017 .

Statistical Analysis with Missing Data

D. Rubin, R. Little, J. Derr, 1988 .

Algorithms for multiple imputation and posterior simulation from incomplete multivariate data with ignorable nonresponse

Donald B. Rubin, Joseph L Schafer, D. Rubin, 1992 .

Multiple Imputation in the Anthrax Vaccine Research Program

D. Rubin, Fan Li, E. Zell, 2010 .

A Statistical Model for Positron Emission Tomography: Comment

D. Rubin, 1985 .

Supplement to “ Comments on the Neyman-Fisher Controversy and its Consequences ”

D. Rubin, Arman Sabbaghi, 2013 .

Visual Revelations: Causal Inference and Death

D. Rubin, H. Wainer, 2015 .

Rejoinder: Principal Stratification and Missing Inputs

D. Rubin, E. Mackenzie, C. Frangakis, 2007 .

Rejoinder : on the duality of missing data and causal effects

D. Rubin, E. Mackenzie, C. Frangakis, 2007 .

Iterative Automated Record Linkage Using Mixture Models

D. Rubin, M. Larsen, M. D. Larsen, 2001 .

Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 2002 .

INFERENCE AND MISSING DATA

D. Rubin, 1975 .

Using Empirical Bayes Techniques in the Law School Validity Studies: Rejoinder

D. Rubin, 1980 .

Statistical Analysis with Missing Data

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 1988 .

BEST PRACTICES IN QUASI- EXPERIMENTAL DESIGNS Matching Methods for Causal Inference

Donald B. Rubin, Elizabeth A. Stuart, D. Rubin, 2007 .

Identification of Causal Effects Using Instrumental Variables: Comment

P. R. Rosembaum, D. Rubin, J. Robins, 1996 .

Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Commentary. Authors' reply

D. Rubin, J. Robins, S. Greenland, 1999 .

Incomplete Data in Epidemiology and Medical Statistics

D. Rubin, E. Zell, Susanne Riissler, 2019 .

Multiple imputation for nonresponse in surveys (book review)

D. Rubin, C. Kershaw, 1989 .

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

D. Rubin, S. Kou, Dongming Huang, 2022, 2208.14123.

Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with “Censoring” Due to Death

D. Rubin, 2006, math/0612783.

Rejoinder to Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with

D. Rubin, 2006, math/0612789.

Imputation

D. Rubin, Susanne Rässler, Elizabeth R. Zell, 2021, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

A Non‐Iterative Algorithm for Least Squares Estimation of Missing Values in Any Analysis of Variance Design

Donald B. Rubin, D. Rubin, 1972 .

A VARIATIONAL CONTROL VARIABLE FOR ASSESSING THE CONVERGENCE OF THE GIBBS SAMPLER

D. Rubin, Chuanhai Liu, Jun Liu, 2022 .

Heterogeneous ozone effects on the DNA methylome of bronchial cells observed in a crossover study

D. Rubin, C. Ward‐Caviness, R. Devlin, 2020, Scientific Reports.

Selection modelling versus mixture modelling with nonignorable nonresponse

Donald B. Rubin, Nan M. Laird, Robert J. Glynn, 1986 .

MEASURING THE APPROPRIATENESS OF MULTIPLE‐CHOICE TEST SCORES1,2

D. Rubin, Michael V. Levine, 1976 .

Maximum-Likelihood Estimation in Panel Studies with Missing Data

Donald B. Rubin, Anthony R. Olsen, D. Rubin, 1980 .

Iteratively Reweighted Least Squares

D. Rubin, 2006 .

A Note on Percent Variance Explained as A Measure of the Importance of Effects

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1979 .

Computationally efficient Markov chain simulation

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

On Lord's Paradox. Program Statistics Research.

Donald B. Rubin, Paul W. Holland, D. Rubin, 1982 .

Multiple Contrasts and Ordered Bonferroni Procedures

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1984 .

High-dimensional randomization-based inference capitalizing on classical design and modern computing

D. Rubin, M. Bind, 2022, Behaviormetrika.

Statistical Analysis: Summarizing Evidence Versus Establishing Facts

D. Rubin, R. Rosenthal, 1985 .

Causal Inference in Prospective and Retrospective Studies.

Donald B. Rubin, Paul W. Holland, D. Rubin, 1980 .

Subclassification on the Propensity Score

D. Rubin, G. Imbens, 2015 .

Modeling accounting for data collection

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

Gaussian process models

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

Improving covariate balance in 2K factorial designs via rerandomization with an application to a New York City Department of Education High School Study

D. Rubin, Tirthankar Dasgupta, Zach Branson, 2016 .

Asymptotics and connections to non-Bayesian approaches

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

Hierarchical linear models

David B. Dunson, Aki Vehtari, Hal S. Stern, 1995 .

CHAPMAN & HALL/CRC Texts in Statistical Science Series

Hal S. Stern, Jeff Harrison, Donald B. Rubin, 2002 .

Special topics in computation

Hal S. Stern, Andrew Gelman, Donald B. Rubin, 1995 .

Basics of Markov chain simulation

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

Introduction to Bayesian computation

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

Large-sample inference and frequency properties of Bayesian inference

Hal S. Stern, Donald B. Rubin, John B. Carlin, 1995 .

Models for missing data

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

Introduction to multiparameter models

David B. Dunson, Hal S. Stern, Andrew Gelman, 2013 .

Connections and challenges

Hal S. Stern, Andrew Gelman, Donald B. Rubin, 1995 .

Overview of computation

Hal S. Stern, Andrew Gelman, Donald B. Rubin, 1995 .

The Effect of Implicit Contracts on the Movement of Wages Over the Business Cycle : Evidence from Micro Data

D. Rubin, J. Angrist, G. Imbens, 2007 .

Models for robust inference

David B. Dunson, Aki Vehtari, Hal S. Stern, 1995 .

Matched Sampling for Causal Effects

D. Rubin, 2006 .

Analyses that Inform Policy Decisions

D B Rubin, R Gutman, D. Rubin, 2012, Biometrics.

Affinely Invariant Matching Methods with Ellipsoidal Distributions

Donald B. Rubin, D. Rubin, N. Thomas, 1992 .

Propensity score methods for creating covariate balance in observational studies

D. Rubin, E. Zell, C. Pattanayak, 2011 .

Bayesian inference for causal effects in randomized experiments with noncompliance

D. Rubin, G. Imbens, 1997 .

Logit-Based Interval Estimation for Binomial Data Using the Jeffreys Prior

Donald B. Rubin, Nathaniel Schenker, D. Rubin, 1987 .

Bayesian causal inference: Approaches to estimating the effect of treating hospital type on cancer survival in Sweden using principal stratification

D. Rubin, Xiaoqin Wang, Li Yin, 2018, Oxford Handbooks Online.

Identification of Causal Effects Using Instrumental Variables

Joshua D. Angrist, Guido W. Imbens, Donald B. Rubin, 1993 .

The Calculation of the Posterior Distribution of the Cell Means in a Two‐Way Unbalanced Manova

Donald B. Rubin, D. Rubin, T. W. F. Stroud, 1977 .

Further issues in effect size estimation for one-sample multiple-choice-type data

D. Rubin, R. Rosenthal, 1991 .

The Analysis of Social Science Data with Missing Values

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 1989 .

Causal inference from 2K factorial designs by using potential outcomes

Tirthankar Dasgupta, Donald B. Rubin, Natesh S. Pillai, 2015 .

Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions

Donald B. Rubin, D. Rubin, 2003, PKDD.

Matching using estimated propensity scores: relating theory to practice.

D B Rubin, N Thomas, D. Rubin, 1996, Biometrics.

Formal modes of statistical inference for causal effects

Donald B. Rubin, D. Rubin, 1990 .

THE VARIANCE OF A LINEAR COMBINATION OF INDEPENDENT ESTIMATORS USING ESTIMATED WEIGHTS

Donald B. Rubin, Sanford Weisberg, D. Rubin, 1974 .

Testing treatment effects in unconfounded studies under model misspecification: Logistic regression, discretization, and their combination

D. Rubin, R. Gutman, M. Cangul, 2009, Statistics in medicine.

Markov chain Monte Carlo methods in biostatistics

D B Rubin, A Gelman, D. Rubin, 1996, Statistical methods in medical research.

Avoiding Model Selection in Bayesian Social Research

Donald B. Rubin, Andrew Gelman, D. Rubin, 1995 .

Sensitivity analysis for a partially missing binary outcome in a two‐arm randomized clinical trial

Donald B Rubin, D. Rubin, Victoria Liublinska, 2014, Statistics in medicine.

‘Clarifying missing at random and related definitions, and implications when coupled with exchangeability’

Donald B. Rubin, Fabrizia Mealli, D. Rubin, 2015 .

Validation of Software for Bayesian Models Using Posterior Quantiles

Donald B. Rubin, Samantha R. Cook, Andrew Gelman, 2006 .

Discussion of “Estimation of Intervention Effects with Noncompliance: Alternative Model Specifications” by Booil Jo

Donald B. Rubin, Fabrizia Mealli, D. Rubin, 2002 .

Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism.

D B Rubin, D. Rubin, 1991, Biometrics.

MISSING DATA IN LARGE DATA SETS

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 1983 .

Efficiently Simulating the Coverage Properties of Interval Estimates

Donald B. Rubin, Nathaniel Schenker, D. Rubin, 1986 .

Public Schools Versus Private Schools: Causal Inference With Partial Compliance

Donald B. Rubin, D. Rubin, Hui Jin, 2009 .

Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions

Donald B. Rubin, Elizabeth A. Stuart, D. Rubin, 2006, math/0611263.

MULTIVARIATE MATCHING METHODS THAT ARE EQUAL PERCENT BIAS REDUCING, I: SOME EXAMPLES

Donald B. Rubin, D. Rubin, 1974 .

Complete‐Case and Available‐Case Analysis, Including Weighting Methods

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 2014, Statistical Analysis with Missing Data, Third Edition.

19 Incomplete Data in Epidemiology and Medical Statistics

Donald B. Rubin, Susanne Rässler, Elizabeth R. Zell, 2007 .

Conceptual, computational and inferential benefits of the missing data perspective in applied and theoretical statistical problems

D. Rubin, 2006 .

MODEL-BASED ANALYSIS TO IMPROVE THE PERFORMANCE OF ITERATIVE SIMULATIONS

Donald B. Rubin, Chuanhai Liu, D. Rubin, 2002 .

Markov-Normal analysis of iterative simulations before their convergence

D. Rubin, Chuanhai Liu, 1996 .

Multiple imputation in health-care databases: an overview and some applications.

D B Rubin, N Schenker, D. Rubin, 1991, Statistics in medicine.

A Single Series from the Gibbs Sampler Provides a False Sense of Security *

D. Rubin, A. Gelman, 2008 .

Estimating and Using Propensity Scores with Partially Missing Data

D. Rubin, R. D'Agostino, 2000 .

Ellipsoidally symmetric extensions of the general location model for mixed categorical and continuous data

D. Rubin, Chuanhai Liu, 1998 .

Automatic detection of influential actors in disinformation networks

Edward K. Kao, Danelle C. Shah, Olga Simek, 2020, Proceedings of the National Academy of Sciences.

Bayesian Inference for Causal Effects

Donald B. Rubin, D. Rubin, 2005 .

Estimating the causal effects of smoking

D B Rubin, D. Rubin, 2001, Statistics in medicine.

Statistical Issues in the Estimation of the Causal Effects of Smoking Due to the Conduct of the Tobacco Industry

Donald B. Rubin, D. Rubin, 2000 .

Nonstandard conditionally specified models for nonignorable missing data

Edoardo M Airoldi, Donald B Rubin, Alexander M Franks, 2020, Proceedings of the National Academy of Sciences.

On principles for modeling propensity scores in medical research

Donald B Rubin, D. Rubin, 2004, Pharmacoepidemiology and drug safety.

Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches.

R J Little, D B Rubin, D. Rubin, 2000, Annual review of public health.

The analysis of repeated-measures data on schizophrenic reaction times using mixture models.

D. Rubin, T. Belin, 1995, Statistics in medicine.

Comment: Assumptions and Procedures in the File Drawer Problem

Donald B. Rubin, Robert Rosenthal, Robert Rosenthal, 1988 .

Criminality in XYY and XXY men.

D B Rubin, K Hirschhorn, K O Christiansen, 1976, Science.

Effect Size Estimation for One-Sample Multiple-Choice-Type Data: Design, Analysis, and Meta-Analysis

D. Rubin, R. Rosenthal, 1989 .

Spatial and object working memory impairments in schizophrenia patients: a Bayesian item-response theory analysis.

Yungtai Lo, Steven Matthysse, John Barnard, 2002, Journal of abnormal psychology.

Multiple Imputation by Ordered Monotone Blocks With Application to the Anthrax Vaccine Research Program

Donald B. Rubin, Fan Li, Fabrizia Mealli, 2014 .

Multiple Imputation in the Anthrax Vaccine Research Program

Samantha R. Cook, Fabrizia Mealli, Fan Li, 2010 .

Discussion of “on State Education Statistics”: A Difficulty with Regression Analyses of Regional Test Score Averages

Donald B. Rubin, Paul R. Rosenbaum, D. Rubin, 1985 .

Essential concepts of causal inference: a remarkable history and an intriguing future

Donald B. Rubin, D. Rubin, 2019, Biostatistics & Epidemiology.

Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self‐Selected Right Censoring

Donald B. Rubin, Constantine Frangakis, D. Rubin, 2001 .

Rejoinder to Discussions on Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self‐Selected Right Censoring

D B Rubin, C E Frangakis, D. Rubin, 2001, Biometrics.

Statistical Matching Using File Concatenation With Adjusted Weights and Multiple Imputations

Donald B. Rubin, D. Rubin, 1986 .

Principal Stratification Designs to Estimate Input Data Missing Due to Death

Ellen MacKenzie, Constantine E Frangakis, Donald B Rubin, 2007, Biometrics.

Probability and inference

D. Rubin, J. Carlin, A. Gelman, 2013 .

Hypothesis: A single clinical trial plus causal evidence of effectiveness is sufficient for drug approval

D. Rubin, L. Sheiner, C. Peck, 2003, Clinical pharmacology and therapeutics.

Empirical bayes estimation of coefficients in the general linear model from data of deficient rank

Donald B. Rubin, Dorothy T. Thayer, Douglas H. Jones, 1983 .

THE GRADUATE MANAGEMENT ADMISSION TEST PREDICTION BIAS STUDY

Dorothy T. Thayer, Douglas H. Jones, D. Rubin, 1981 .

The Counternull Value of an Effect Size: A New Statistic

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1994 .

Bridging observational studies and randomized experiments by embedding the former in the latter

Donald B Rubin, Marie-Abele C Bind, D. Rubin, 2017, Statistical methods in medical research.

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

D. Rubin, P. Austin, N. Thomas, 2021, Statistics in medicine.

Covariate-adjusted survival analyses in propensity-score matched samples: Imputing potential time-to-event outcomes

D. Rubin, P. Austin, N. Thomas, 2018, Statistical methods in medical research.

Matched Sampling for Causal Effects: Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation

D. Rubin, 2006 .

Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data

Donald B. Rubin, Fabrizia Mealli, Barbara Pacini, 2012 .

Bayesian Break-Point Forecasting in Parallel Time Series, with Application to University Admissions

D. B. Rubin, D. Rubin, T. W. F. Stroud, 1987 .

Computational aspects of analysing random effects/longitudinal models.

D B Rubin, D. Rubin, 1992, Statistics in medicine.

Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic

D B Rubin, P K Hopke, C Liu, 2001, Biometrics.

The NHANES III multiple imputation project

Roderick J. A. Little, Donald B. Rubin, Joseph L Schafer, 1996 .

THE EFFECTIVENESS OF COACHING FOR THE SAT: REVIEW AND REANALYSIS OF RESEARCH FROM THE FIFTIES TO THE FTC

Donald A. Rock, Donald B. Rubin, William H. Angoff, 1980 .

The fragility of standard inferential approaches in principal stratification models relative to direct likelihood approaches

Donald B. Rubin, Fabrizia Mealli, Barbara Pacini, 2016, Stat. Anal. Data Min..

Diagnostics for confounding in PK/PD models for oxcarbazepine

D. Rubin, J. Nedelman, L. Sheiner, 2007, Statistics in medicine.

Bayes and Multiple Imputation

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 2014, Statistical Analysis with Missing Data, Third Edition.

Tasks in statistical inference for studying variation in medicine.

D B Rubin, D. Rubin, 1993, Medical care.

REGULAR ASSIGNMENT MECHANISMS: ANALYSIS

Donald B. Rubin, Guido W. Imbens, D. Rubin, 2015 .

Rubin Causal Model

Donald B. Rubin, L. John, D. Rubin, 2011, International Encyclopedia of Statistical Science.

Evaluating the effects of job training programs on wages through principal stratification

D. Rubin, Junni L. Zhang, F. Mealli, 2008 .

Assumptions when Analyzing Randomized Experiments with Noncompliance and Missing Outcomes

Donald B. Rubin, Fabrizia Mealli, D. Rubin, 2002, Health Services and Outcomes Research Methodology.

Characterizing the effect of matching using linear propensity score methods with normal distributions

Donald B. Rubin, D. Rubin, N. Thomas, 1992 .

Design and Modeling in Conjoint Analysis with Partial Profiles

Donald B. Rubin, D. Rubin, 2004 .

Working Memory Impairments in Schizophrenia Patients: A Bayesian Bivariate IRT Analysis

Yungtai Lo, Donald B. Rubin, Deborah L. Levy, 2002 .

Modeling schizophrenic behavior using general mixture components.

D. Rubin, Y. Wu, 1997, Biometrics.

Studies Teaching Statistical Inference for Causal Effects in Experiments and Observational

Donald B. Rubin, D. Rubin, 2012 .

Matched Sampling for Causal Effects: William G. Cochran's Contributions to the Design, Analysis, and Evaluation of Observational Studies

Donald B. Rubin, D. Rubin, 2006 .

Rejoinder to discussions on clustered encouragement designs with individual noncompliance: Bayesian inference with randomization, and application to advance directive forms

Donald B. Rubin, Xiao-Hua Zhou, Constantine Frangakis, 2002 .

School Choice in NY City: A Bayesian Analysis of an Imperfect Randomized Experiment

D. Rubin, Jennifer L. Hill, C. Frangakis, 2001 .

ON LORD'S PARADOX

D. Rubin, P. Holland, 1982 .

The broad role of multiple imputation in statistical science

Donald B. Rubin, D. Rubin, 2000 .

Characterizing the Estimation of Parameters in Incomplete-Data Problems

Donald B. Rubin, D. Rubin, 1974 .

MISSING AT RANDOM - WHAT DOES IT MEAN?

Donald B. Rubin, D. Rubin, 1973 .

A Modified General Location Model for Noncompliance With Missing Data

D. Rubin, J. Barnard, Hui Jin, 2010 .

Incomplete Data - Encyclopedia Entry.

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 1982 .

A stimulation study to evaluate the performance of model-based multiple imputations in HCHS health examination surveys

Roderick J. A. Little, Donald B. Rubin, Joseph L Schafer, 1995 .

Algorithm AS 120: A Fortran Algorithm for the Additive Model in a Two-Way Unbalanced Manova

Donald B. Rubin, Dorothy T. Thayer, D. Rubin, 1978 .

Fitting Additive Models to Unbalanced Two-Way Data

Dorothy T. Thayer, D. Rubin, T. Stroud, 1981 .

Comparing High Schools with Respect to Student Performance in University

D. Rubin, T. Stroud, 1977 .

Evaluating and Using Statistical Methods in the Social Sciences

Donald B. Rubin, Andrew Gelman, D. Rubin, 1999 .

AN ALGORITHM FOR THE POSTERIOR DISTRIBUTION OF THE CELL MEANS IN A TWO‐WAY UNBALANCED MANOVA

D. Rubin, T. Stroud, 1974 .

Selection Modeling Versus Mixture Modeling with Nonignorable Nonresponse

Donald B. Rubin, Nan M. Laird, Robert J. Glynn, 1986 .

Multiple imputations in sample surveys

Donald B. Rubin, D. Rubin, 1978 .

Noniterative Least Squares Estimates, Standard Errors and F‐Tests for Analyses of Variance with Missing Data

Donald B. Rubin, D. Rubin, 1976 .

EFFECT OF INCREASED TEST‐TAKING TIME ON TEST SCORES BY ETHNIC GROUP, YEARS OUT OF SCHOOL, AND SEX

Donald B. Rubin, Robert A. Durso, Cheryl L. Wild, 1982 .

Simulation Modeling for Cost Estimation

Donald B. Rubin, Andrew Gelman, Richard P. Waterman, 2000 .

A Broader Template for Analyzing Broken Randomized Experiments

Jennifer Hill, Donald B. Rubin, John Barnard, 1998 .

Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

D. Rubin, 2004 .

ESTIMATING CAUSAL EFFECTS OF TREATMENTS IN EXPERIMENTAL AND OBSERVATIONAL STUDIES

D. Rubin, 1972 .

Incomplete data in sample surveys. Vol. 2: theory and bibliographies

Ingram Olkin, Donald B. Rubin, William G. Madow, 1983 .

Hierarchical logistic regression models for imputation of unresolved enumeration status in undercount estimation.

D. Rubin, J. Schafer, A. Zaslavsky, 1993, Journal of the American Statistical Association.

Incomplete data in sample surveys

Ingram Olkin, Donald B. Rubin, William G. Madow, 1985 .

RERANDOMIZATION TO IMPROVE COVARIATE BALANCE IN EXPERIMENTS1 BY KARI

D. Rubin, LOCK MORGAN, DONALD B. RUBIN, 2012 .

Rubin Causal Model

Donald B. Rubin, Guido W. Imbens, D. Rubin, 2011, International Encyclopedia of Statistical Science.

Comparison of Mortality Rates for Three Smoking Groups in Three Databases *

D. Rubin, 2009 .

Matching methods for causal inference: Designing observational studies

Donald B. Rubin, Elizabeth A. Stuart, D. Rubin, 2007 .

Basic Concepts of Statistical Inference for Causal Effects in Experiments and Observational Studies

Donald B. Rubin, D. Rubin, 2004 .

Did the Military Interventions in the Mexican Drug War Increase Violence?

D. Rubin, Valeria Espinosa, 2015 .

Evaluating the Validity of Post-Hoc Subgroup Inferences: A Case Study

Donald B. Rubin, D. Rubin, Joseph J Lee, 2016 .

Missing Data and Imputation Methods

Donald B. Rubin, Alessandra Mattei, Fabrizia Mealli, 2011 .

Handling “Don't Know” Survey Responses: The Case of the Slovenian Plebiscite

D. Rubin, H. Stern, V. Vehovar, 1995 .

A Powerful, Potential Outcomes Method for Estimating Any Estimand across Multiple Groups.

Donald B. Rubin, Elizabeth R. Zell, Cassandra Wolos Pattanayak, 2013 .

Comment : Neyman ( 1923 ) and Causal Inference in Experiments and Observational Studies

Donald B. Rubin, D. Rubin, 2007 .

Individual privacy versus public good: protecting confidentiality in health research

Donald B Rubin, Christine M O'Keefe, D. Rubin, 2015, Statistics in medicine.

When possible, report a Fisher-exact P value and display its underlying null randomization distribution

D. Rubin, M. Bind, 2020, Proceedings of the National Academy of Sciences.

Imputing Income in the CPS: Comments on "Measures of Aggregate Labor Cost in the United States"

Donald Rubin, D. Rubin, 1983 .

Missing Data in Experiments

D. Rubin, R. Little, 2014, Statistical Analysis with Missing Data, Third Edition.

Efficient Methods for Estimation and Testing with Seemingly Unrelated Regressions in the Presence of Latent Variables and Missing Observations

Donald B. Rubin, Xiao-Li Meng, Xiao-Li Meng, 1996 .

Further issues in summarizing 345 studies of interpersonal expectancy effects

D. Rubin, R. Rosenthal, 1980, Behavioral and Brain Sciences.

Test Equating From Biased Samples, With Application to the Armed Services Vocational Aptitude Battery

D. Rubin, R. Little, 1994 .

Intermittent degradation in performance in schizophrenia

D. Rubin, D. Levy, Y. Wu, 1999, Schizophrenia Research.

Diagnosing missing always at random in multivariate data

Iavor Bojinov, Natesh Pillai, Donald Rubin, 2017, Biometrika.

Fisher, Neyman, and Bayes at FDA

Donald B Rubin, D. Rubin, 2016, Journal of biopharmaceutical statistics.

Conditions for Ignoring the Missing-Data Mechanism in Likelihood Inferences for Parameter Subsets

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 2017 .

Combining Panel Data Sets with Attrition and Refreshment Samples

Donald B. Rubin, Guido W. Imbens, Geert Ridder, 1998 .

Nested multiple imputation of NMES via partially incompatible MCMC

Donald B. Rubin, D. Rubin, 2003 .

Multiple Imputation Methods

Donald B. Rubin, Nathaniel Schenker, John Barnard, 2005 .

Multiple imputation in mixture models for nonignorable nonresponse with follow-ups

Donald B. Rubin, Nan M. Laird, Robert J. Glynn, 1993 .

The importance of having a conceptual stage when reporting non-randomized studies

D. Rubin, M. Bind, 2021, Biostatistics & epidemiology.

Comparing Within- and Between-Subjects Studies

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1980 .

Imputation and multiple imputation

D. Rubin, N. Schenker, 2009 .

Using Mixture Models in Temperament Research

Hal S. Stern, Donald B. Rubin, Jerome Kagan, 1995 .

On the limitations of comparative effectiveness research

Donald B Rubin, D. Rubin, 2010, Statistics in medicine.

[Propensity score methods for creating covariate balance in observational studies].

Donald B Rubin, D. Rubin, E. Zell, 2011, Revista espanola de cardiologia.

Fisher's Exact P-Values for Completely Randomized Experiments

D. Rubin, G. Imbens, 2015 .

More powerful randomization-based p-values in double-blind trials with non-compliance.

D B Rubin, D. Rubin, 1998, Statistics in medicine.

Sensitivity of Bayes Inference with Data-Dependent Stopping Rules

Donald B. Rubin, Paul R. Rosenbaum, D. Rubin, 1984 .

Some applications of Bayesian statistics to educational data

Donald B. Rubin, D. Rubin, 1983 .

Influence Estimation on Social Media Networks Using Causal Inference

Steven Thomas Smith, Edward K. Kao, Danelle C. Shah, 2018, 2018 IEEE Statistical Signal Processing Workshop (SSP).

Bayesian Data Analysis.@@@Bayes and Empirical Bayes Methods for Data Analysis.

D. Rubin, J. Carlin, T. Louis, 1997 .

A hierarchical finite mixture model that accommodates zero‐inflated counts, non‐independence, and heterogeneity

D. Rubin, D. Levy, C. Morgan, 2014, Statistics in medicine.

Single Imputation Methods

Roderick J. A. Little, Donald B. Rubin, D. Rubin, 2014, Statistical Analysis with Missing Data, Third Edition.

Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse

D. Rubin, R. Little, 2014, Statistical Analysis with Missing Data, Third Edition.

Matched Sampling for Causal Effects: Multivariate Matching Methods That Are Equal Percent Bias Reducing, II: Maximums on Bias Reduction for Fixed Sample Sizes

D. Rubin, 2006 .

PCA Rerandomization

D. Rubin, G. Yin, Hengtao Zhang, 2021, Canadian Journal of Statistics.

Comment: The Design and Analysis of Gold Standard Randomized Experiments

Donald B. Rubin, D. Rubin, 2008 .

The Design of a General and Flexible System for Handling Nonresponse in Sample Surveys

Donald B. Rubin, D. Rubin, 2004 .

STATISTICAL CHOICES IN INFANT TEMPERAMENT RESEARCH

D. Rubin, J. Kagan, H. Stern, 1994 .

NONITERATIVE LEAST SQUARES ESTIMATES, STANDARD ERRORS, AND F-TESTS FOR ANY ANALYSIS OF VARIANCE WITH MISSING DATA

D. Rubin, 1974 .

Projecting From Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics

Roderick J. A. Little, Donald B. Rubin, John L. Czajka, 1992 .

Comment: Assessing the Fit of Logistic Regressions Using the Implied Discriminant Analysis

Donald B. Rubin, D. Rubin, 1984 .

Assessing the Potential Impact of a Nationwide Class-Based Affirmative Action System

D. Rubin, Alice Xiang, 2015, 1509.03397.

Estimation of causal effects of binary treatments in unconfounded studies with one continuous covariate

D. Rubin, D. Rubin, R. Gutman, 2017, Statistical methods in medical research.

Gaussian process models

D. Rubin, J. Carlin, A. Gelman, 2013 .

A Case Study of the Robustness of Bayesian Methods of Inference: Estimating the Total in a Finite Population Using Transformations to Normality

Donald B. Rubin, D. Rubin, 1983 .

Expert Report on Multiple Imputation

D. Rubin, 2005 .

MAXIMUM LIKELIHOOD ESTIMATION OF FACTOR ANALYSIS USING THE ECME ALGORITHM WITH COMPLETE AND INCOMPLETE DATA

D. Rubin, Chuanhai Liu, 1998 .

Correction for Huang et al., Catalytic prior distributions with application to generalized linear models

D. Rubin, S. Kou, Dongming Huang, 2020, Proceedings of the National Academy of Sciences.

Measuring the Appropriateness of Multiple-Choice Test Scores

Donald B. Rubin, Michael V. Levine, Michael V. LeVine, 1979 .

Procedures with Nonignorable Nonresponse

D. Rubin, 2008 .

Nonignorable Missing‐Data Models

D. Rubin, R. Little, 2014 .

Parameter expansion to accelerate EM : The PX-EM algorithm

D. Rubin, Y., Ñ., 1997 .

Principal stratification approach to broken randomized experiments: A case study of school choice vouchers in New York City. Comments. Authors' reply

D. Rubin, R. Berk, B. Muthén, 2003 .

Matched Sampling for Causal Effects: The Central Role of the Propensity Score in Observational Studies for Causal Effects

D. Rubin, P. Rosenbaum, 2006 .

Permutation tests for detecting and estimating mixtures in task performance within groups

D. Rubin, Y. Lo, P. Holzman, 2002, Statistics in medicine.

Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies

D. Rubin, 1973 .

Propensity score methods.

D. Rubin, 2010, American journal of ophthalmology.

Comment: Self-Experimentation for Causal Effects

Donald B. Rubin, D. Rubin, 2001 .

Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies

D. Rubin, 1973 .

Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling.

D. Rubin, G. McLachlan, M. Lenzenweger, 2007, Journal of abnormal psychology.

Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

D. Rubin, P. Rosenbaum, 1984 .

Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies

D. Rubin, 1973 .

Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates

D. Rubin, Neal Thomas, 2000 .

Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies

D. Rubin, 1973 .

Randomization to Randomization Probability: Estimating Treatment Effects Under Actual Conditions of Use

D. Rubin, D. Allison, H. Lieberman, 2017, Psychological methods.

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

D. Rubin, S. Kou, Dongming Huang, 2022, 2208.14123.

On optimal rerandomization designs

Donald B. Rubin, Per Johansson, Mårten Schultzberg, 2021, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

A METHOD FOR FORMALIZING SUBJECTIVE NOTIONS ABOUT THE EFFECT OF NON‐RESPONDENTS IN SAMPLE SURVEYS

D. Rubin, 1975 .

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

D. Rubin, G. Imbens, 2015 .

Reply to Discussion

D. Rubin, E. Arjas, 2004 .

Statistical Analysis with Missing Data, Third Edition

D. Rubin, R. Little, 2019, Wiley Series in Probability and Statistics.

Identification of Causal Effects Using Instrumental Variables: Comment

James J. Heckman, Joshua D. Angrist, Donald B. Rubin, 1996 .

Comment Jianqing Fan and Chunming Zhang 2. Validation of Assumptions on Drop-out Time Comment Mark Van Der Laan

D. Rubin, D. Freedman, R. Little, 1999 .

Statistical Analysis with Missing Data.

D. Rubin, R. Little, Martin G. Gibson, 1989 .

THE ACCEPTABILITY OF REGRESSION SOLUTIONS: ANOTHER LOOK AT COMPUTATIONAL ACCURACY

Donald B. Rubin, Albert E. Beaton, John L. Barone, 1972 .

Matched Sampling for Causal Effects: Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies

D. Rubin, 2006 .

Relating tests given to different samples

Donald B. Rubin, Dorothy T. Thayer, D. Rubin, 1978 .

C. R. Rao's century

D. Rubin, S. Amari, B. Efron, 2020, Significance.

Estimation of Imputation Uncertainty

D. Rubin, R. Little, 2014 .

Pseudo-Opinion Polls: SLOP or Useful Data?: Statistical Cryptic

Donald B. Rubin, Tom Sawyer, Judith M. Tanur, 1995 .

Discussion of: 'Causal Diagrams for Empirical Research' by J. Pearl

D. Rubin, G. Imbens, 1995 .

Underlying Bayesian Theory

D. Rubin, 2008 .

Matched Sampling for Causal Effects: Contributor Acknowledgments

D. Rubin, 2006 .

Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Commentary. Authors' reply

Larry Wasserman, D. Rubin, J. Robins, 1999 .

Disentangling Treatment and Placebo Effects in Randomized Experiments Using Principal Stratification—An Introduction

D. Rubin, Reagan Mozer, R. Kessels, 2017 .

Propensity score methods for creating covariate balance in observational studies

D. Rubin, E. Zell, C. Pattanayak, 2011 .

Characterizing the effect of matching using linear propensity score methods with normal distributions

D. Rubin, Neal Thomas, 1992 .

A Note on Bayesian, Likelihood, and Sampling Distribution Inferences

Donald B. Rubin, D. Rubin, 1978 .

Ensemble-Adjusted p Values

Donald B. Rubin, Robert Rosenthal, D. Rubin, 1983 .

Research Designs and Causal Inferences: On Lord’s Paradox

D. Rubin, P. Holland, 1986 .

Bayesian Data Analysis

W. J. Thompson, T. C. Black, D. Rubin, 2024, The SAGE Encyclopedia of Research Design.

Causal Inference for Statistics, Social, and Biomedical Sciences: Instrumental Variables Analysis of Randomized Experiments with Two-Sided Noncompliance

D. Rubin, G. Imbens, 2015 .

Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010).

D. Rubin, 2010, Psychological methods.

Finding maximum likelihood estimates of patterned covariance matrices by the EM algorithm

Donald B. Rubin, Ted H. Szatrowski, D. Rubin, 1982 .

Causal Inference for Statistics, Social, and Biomedical Sciences: Sensitivity Analysis and Bounds

D. Rubin, G. Imbens, 2015 .

A Bayesian Perspective on the Analysis of Unreplicated Factorial Experiments Using Potential Outcomes

Donald B. Rubin, Tirthankar Dasgupta, Valeria Espinosa, 2016, Technometrics.

A Potential Outcomes, and Typically More Powerful, Alternative to 'Cochran-Mantel-Haenszel'

D. Rubin, E. Zell, C. Pattanayak, 2012 .

Valid randomization‐based p‐values for partially post hoc subgroup analyses

D. Rubin, Joseph J Lee, 2015, Statistics in Medicine.