Paul R. Rosenbaum

Paul R. Rosenbaum
  • Robert G. Putzel Professor Emeritus of Statistics and Data Science

Contact Information

  • office Address:

    313 Academic Research Building
    265 South 37th 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
IMS Medallion Lecture, 2020
Long-Term Excellence Award from the Health Policy Statistics Section of the American Statistical Association, 2018
Nathan Mantel Award from the Section on Statistics in Epidemiology of the American Statistical Association, 2017

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

Continue Reading

Research

  • Paul R. Rosenbaum, Replication and Evidence Factors in Observational Studies (Chapman and Hall/CRC Monographs on Statistics and Applied Probability) (2021)

    Abstract: Book in the series: Chapman and Hall/CRC Monographs on Statistics and Applied Probability.

  • Bikram Karmakar, Dylan Small, Paul R. Rosenbaum (2021), Reinforced Designs: Multiple Instruments Plus Control Groups as Evidence Factors in an Observational Study of the Effectiveness of Catholic Schools, Journal of the American Statistical Association, 116 (533), pp. 82-92. 10.1080/01621459.2020.1745811

    Abstract: Absent randomization, causal conclusions gain strength if several independent evidence factors concur. We develop a method for constructing evidence factors from several instruments plus a direct comparison of treated and control groups, and we evaluate the methods performance in terms of design sensitivity and simulation. In the application, we consider the effectiveness of Catholic versus public high schools, constructing three evidence factors fromthree past strategies for studying this question, namely: (i) having nearby access to a Catholic school as an instrument, (ii) being Catholic as an instrument for attending Catholic school, and (iii) a direct comparison of students in Catholic and public high schools. Although these three analyses use the same data,we: (i) construct three essentially independent statistical tests of no effect that require very different assumptions, (ii) study the sensitivity of each test to the assumptions underlying that test, (iii) examine the degree to which independent tests dependent upon different assumptions concur, (iv) pool evidence across independent factors. In the application, we conclude that the ostensible benefit of Catholic education depends critically on the validity of one instrument, and is therefore quite fragile.

  • Paul R. Rosenbaum (2020), Combining planned and discovered comparisons in observational studies, Biostatistics, 21 (3), pp. 384-399. 10.1093/biostatistics/kxy055

  • 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).

  • Ruoqi Yu, Jeffrey H. Silber, Paul R. Rosenbaum (2020), Matching methods for observational studies derived from large administrative databases, Statistical Science, 18. 10.1214/19-STS699

    Abstract: (Authors: Ruoqi Yu, Jeffrey Silber, and Paul R. Rosenbaum) We propose new optimal matching techniques for large administrative data sets. In current practice, very large matched samples are constructed by subdividing the population and solving a series of smaller problems, for instance, matching men to men and separately matching women to women. Without simplification of some kind, the time required to optimally match T treated individuals to T controls selected from C ≥ T potential controls grows much faster than linearly with the number of people to be matched—the required time is of order O{(T +C)^3}—so splitting one large problem into many small problems greatly accelerates the computations. This common practice has several disadvantages that we describe. In its place, we propose a single match, using everyone, that accelerates the computations in a different way. In particular, we use an iterative form of Glover’s algorithm for a doubly convex bipartite graph to determine an optimal caliper for the propensity score, radically reducing the number of candidate matches; then we optimally match in a large but much sparser graph. In this graph, a modified form of near-fine balance can be used on a much larger scale, improving its effectiveness. We illustrate the method using data from US Medicaid, matching children receiving surgery at a children’s hospital to similar children receiving surgery at a hospital that mostly treats adults. In the example, we form 38,841 matched pairs from 159,527 potential controls, controlling for 29 covariates plus 463 Principal Surgical Procedures, plus 973 Principal Diagnoses. The method is implemented in an R package bigmatch available from CRAN.

  • 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.

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

In the News

Knowledge @ Wharton

Activity

In the News

Grading Hospital Quality with a Level Playing Field

By “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
All News