Paul R. Rosenbaum
- Robert G. Putzel Professor
- Professor of Statistics
Contact Information
- Primary Email:
rosenbaum@wharton.upenn.edu - Office Phone:
(215) 898-3120
- office Address:
473 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104
Research Interests: design and analysis of observational studies, design and analysis of experiments, health outcomes research
Links: CV, Personal Website
Overview
Education
PhD, Harvard University, 1980
AM, Harvard University, 1978
BA, Hampshire College, 1977
Career and Recent Professional Awards
R. A. Fisher Award and Lecture from the Committee of Presidents of Statistical Societies, 2019
George W. Snedecor Award from the Committee of Presidents of Statistical Societies, 2003
Academic Positions Held
Wharton: 1986-present. (named Robert G. Putzel Professor, 2001; Robert B. Egelston Term Professor of Statistics, 1991-92; Joseph Wharton Term Associate Professor and Professor of Statistics, 1986-91)
Previous appointment: University of Wisconsin, Madison
Other Positions
Senior Research Scientist, Research Statistics Group, Educational Testing Service, 1986
Research Scientist, Research Statistics Group, Educational Testing Service, 1983-86
Statistician, Division of Statistics and Applied Mathematics, Office of Radiation Programs, U.S. Environmental Protection Agency, 1980-81
Professional Leadership
Member, Committee on National Statistics, National Research Council, 1996-99
Member, Committee on Data and Research for Policy on Illegal Drugs, National Research Council, 1998-2000
Member, Advisory Board of the Measurement, Methodology and Statistics Program of the U.S. National Science Foundation, 1999-2001
For more information, go to My Personal Page
Research
Bikram Karmakar, Dylan Small, Paul R. Rosenbaum (2020), Using evidence factors to clarify exposure biomarkers, American Journal of Epidemiology, (to appear).
Paul R. Rosenbaum (2020), Combining planned and discovered comparisons in observational studies, Biostatistics, (to appear).
Rachel R. Kelz, Morgan M. Sellers, Bijan A. Niknam, James E. Sharpe, Paul R. Rosenbaum, Alexander S. Hill, Hong Zhou, Lauren L. Hochman, Karl Y. Bilimoria, Kamal Itani, Patrick S. Romano, Jeffrey H. Silber (2020), A national comparison of operative outcomes of new and experienced surgeons, Annals of Surgery, (to appear).
Paul R. Rosenbaum, Design of Observational Studies, 2nd edition (Springer, 2020)
Abstract: ProductFlyer_9783030464042
Raiden B. Hasegawa, Sameer K. Deshpande, Dylan Small, Paul R. Rosenbaum (2020), Causal inference with two versions of treatment, Journal of Educational and Behavioral Statistics, (to appear).
Rachel R. Kelz, Bijan A. Niknam, Morgan M. Sellers, James E. Sharpe, Paul R. Rosenbaum, Alexander S. Hill, Hong Zhou, Lauren L. Hochman, Karl Y. Bilimoria, Kamal Itani, Patrick S. Romano, Jeffrey H. Silber (2020), Duty hour reform and the outcomes of patients treated by new surgeons, Annals of Surgery, 271 (4), pp. 599-605.
Jeffrey H. Silber, Paul R. Rosenbaum, Bijan A. Niknam, Richard N. Ross, Joseph G. Reiter, Alexander S. Hill, Lauren L. Hochman, Sydney E. Brown, Alexander F. Arriaga, Lee A. Fleisher (2020), Comparing outcomes and costs of medical patients treated at major teaching and non-teaching hospitals: a national matched analysis, Journal of General Internal Medicine, 35 (3), pp. 743-752.
Paul R. Rosenbaum (2020), Modern algorithms for matching in observational studies, Annual Review of Statistics and Its Application , 7, pp. 143-176.
Jeffrey H. Silber, Paul R. Rosenbaum, Bijan A. Niknam, Richard N. Ross, Joseph G. Reiter, Alexander S. Hill, Lauren L. Hochman, Sydney E. Brown, Alexander F. Arriaga, Rachel R. Kelz, Lee A. Fleisher (2020), Comparing outcomes and costs of surgical patients treated at major teaching and nonteaching hospitals: a national matched analysis, Annals of Surgery, 271 (3), pp. 412-421.
Ruoqi Yu and Paul R. Rosenbaum (2019), Directional penalties for optimal matching in observational studies, Biometrics, 75 (4), pp. 1380-1390.
Teaching
Past Courses
BSTA550 - APPLIED REG & ANALY VAR
An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
PSYC611 - APPLIED REG & ANALY VAR
An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
PSYC612 - INT TO NONP & LOGLIN MOD
An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
STAT112 - INTRODUCTORY STATISTICS
Further development of the material in STAT 111, in particular the analysis of variance, multiple regression, non-parametric procedures and the analysis of categorical data. Data analysis via statistical packages. This course may be taken concurrently with the prerequisite with instructor permission.
STAT500 - APPLIED REG & ANALY VAR
An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
STAT501 - INT TO NONP & LOGLIN MOD
An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
STAT991 - SEM IN ADV APPL OF STAT
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
STAT995 - DISSERTATION
STAT999 - INDEPENDENT STUDY
Written permission of instructor and the department course coordinator required to enroll.
Awards and Honors
- Long-Term Excellence Award from the Health Policy Statistics Section of the American Statistical Association, 2018
- C. G. Khatri Lecture, Penn State University, 2017
- Nathan Mantel Award from the Section on Statistics in Epidemiology of the American Statistical Association, 2017
- Nelder Lecture, Imperial College, 2016
- George W. Snedecor Award from COPSS, 2003
- Fellow, American Statistical Association, 1992
- R.A. Fisher Award and Lecture from COPSS, 2019
In the News
Knowledge @ Wharton
- Grading Hospital Quality with a Level Playing Field, Knowledge @ Wharton - 5/20/2014
Activity
Latest Research
In the News
Grading Hospital Quality with a Level Playing FieldBy “matching” patients across a group of hospitals, a new paper co-authored by two Wharton professors suggests a fairer and more accurate way of assessing the quality of health care providers.
Knowledge @ Wharton - 5/20/2014