Paul R. Rosenbaum

• Paul R. Rosenbaum, An Introduction to the Theory of Observational Studies (Gewerbestrasse 11, 6330 Cham, Switzerland: Springer, 2025) Abstract

  This book is an introduction to the theory of causal inference in observational studies.   An observational study draws inferences about the effects caused by treatments or preventable exposures when randomized experimentation is unethical or infeasible.  An observational study is distinguished from an experiment by the problems that follow from the absence of randomized assignment of individuals to treatments.  Observational studies are common in most fields that study the effects of treatments or policies on people, including public health and epidemiology, economics and public policy, medicine and clinical psychology, and criminology and empirical legal studies.

   

   

  Description
  After Part I reviews causal inference in randomized experiments, the twelve short chapters in Parts II, III and IV introduce modern topics: the propensity score, ignorable treatment assignment, the principal unobserved covariate, algorithms for optimal matching, randomized reassignment techniques for appraising the covariate balance achieved by matching, covariance adjustment, sensitivity analysis, design sensitivity, ways to design an observational study to be insensitive to larger unmeasured biases, the large sample efficiency of a sensitivity analysis, quasi-experimental devices that provide observable information about unmeasured biases, evidence factors and complementary analyses to address unmeasured biases.   The book is accessible to anyone who has completed an undergraduate course in mathematical statistics.  The subject is developed with the aid of two simple empirical examples concerning the health benefits or harms caused by consuming alcohol.  The data for these examples and their reanalyses are freely available in an R package, iTOS, associated with Introduction to the Theory of Observational Studies.
• Ruoqi Yu, Dylan Small, Paul R. Rosenbaum (2021), The Information in Covariate Imbalance in Studies of Hormone Replacement Therapy, Annals of Applied Statistics, 15 (4), pp. 2023-2042.
• Ruoqi Yu, Dylan Small, David Harding, Jose Alvarez, Paul R. Rosenbaum (2021), Optimal Matching for Observational Studies that Integrate Quantitative and Qualitative Research, Statistics and Public Policy , 8 (p.p. 42-52).
• 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.

• Siddharth Jain, Paul R. Rosenbaum, Joseph Reiter, Geoffrey Hoffman, Dylan Small, Jinkyung Ha, Alexander S. Hill, David A. Wolk, Timothy G. Gaulton, Mark Neuman, Roderic G. Eckenhoff, Lee Fleisher, Jeffrey H. Silber (2021), Using Medicare claims in identifying Alzheimer’s disease and related dementias, Alzheimer's & Dementia, 17 (3), pp. 515-524.
• 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. 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.

  Related
  Links
• 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

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Past Courses

• BSTA5500 - 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.

• PSYC6110 - 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.

• PSYC6120 - 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.

• STAT5000 - 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.

• STAT5010 - 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.

• STAT9950 - Dissertation

  Dissertation

• Medallion Lecture, Institute of Mathematical Statistics, 2020
• 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, 1970

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