Dylan Small

Dylan Small
  • Universal Furniture Professor
  • Professor of Statistics and Data Science
  • Department Chair

Contact Information

  • office Address:

    417 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: applications of statistics to public health, design and analysis of experiments and observational studies for comparing treatments, longitudinal data, measurement error, medicine and economics

Overview

Education

PhD, Stanford University, 2002
BA, Harvard University, 1997

Academic Positions Held

Wharton: 2002-present

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Research

Teaching

Past Courses

  • AMCS5999 - Independent Study

    Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • BSTA6990 - Lab Rotation

    Student lab rotation.

  • MATH4990 - Supervised Study

    Study under the direction of a faculty member. Intended for a limited number ofmathematics majors.

  • STAT3990 - Independent Study

    Written permission of instructor and the department course coordinator required to enroll in this course.

  • STAT4920 - Community Data Science

    This course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups of 3-5 on a data science problem of interest to a community organization. Problems that may be considered include working with the Netter Center on • Nonpartisan voter engagement on the Penn campus and in the surrounding community • The relationship between the educational requirements of jobs that are available in the West Philadelphia community and education in the community • Heat islands in West Philadelphia • Improving patient care at Hospital University of Pennsylvania Cedar Avenue This is an Academically Based Community Service (ABCS) course. Prerequisites: The course presumes that students have taken a sequence of introductory statistics courses such as STAT 1010/1020, or 4300/4310 and that they have taken a course that has exposed them to more advanced techniques such as STAT 4220, 4230, 4420, 4710 or 4730. It will be assumed that students have knowledge of a statistical programming language such as R or Python. Classes such as STAT 4050, 4700, 4770 or 4800 would meet this requirement.

  • STAT5900 - Causal Inference

    Questions about cause are at the heart of many everyday decisions and public policies. Does eating an egg every day cause people to live longer or shorter or have no effect? Do gun control laws cause more or less murders or have no effect? Causal inference is the subfield of statistics that considers how we should make inferences about such questions. This course will cover the key concepts and methods of causal inference rigorously. Background in probability and statistics; some knowledge of R is recommended.

  • STAT5920 - Community Data Science

    This course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups of 3-5 on a data science problem of interest to a community organization. Problems that may be considered include working with the Netter Center on • Nonpartisan voter engagement on the Penn campus and in the surrounding community • The relationship between the educational requirements of jobs that are available in the West Philadelphia community and education in the community • Heat islands in West Philadelphia • Improving patient care at Hospital University of Pennsylvania Cedar Avenue This is an Academically Based Community Service (ABCS) course. Prerequisite: The course presumes that students have taken a sequence of introductory statistics courses such as STAT 1010/1020, or 4300/4310 and that they have taken a course that has exposed them to more advanced techniques such as STAT 4220, 4230, 4420, 4710 or 4730. It will be assumed that students have knowledge of a statistical programming language such as R or Python. Classes such as STAT 4050, 4700, 4770 or 4800 would meet this requirement.

  • STAT9210 - Observational Studies

    This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching and propensity score methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.

  • STAT9620 - Adv Methods Applied Stat

    This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.

  • STAT9710 - Intro To Linear Stat Mod

    Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.

  • STAT9910 - Sem in Adv Appl of Stat

    This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.

  • STAT9915 - Sem in Adv Appl of Stat

    This seminar-based course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups on a data science problem of interest to a community organization.

  • STAT9950 - Dissertation

    Dissertation

  • STAT9999 - Independent Study

    Written permission of instructor and the department course coordinator required to enroll.

Awards And Honors

  • IMS Medallion Lecturer, 2022
  • Steve Feinberg Memorial Lecture Series in Advanced Analytics Lecture, Carnegie Mellon University, 2018
  • Fellow, American Statistical Association, 2013

In the News

Knowledge @ Wharton

Activity

Latest Research

Dane Isenberg, Edward Kennedy, Richard Landis, Nandita Mitra, James M. Robins, Jason Roy, Alisa J. Stephens-Shields, Wei Yang, Dylan Small (2024), Marshall Joffe’s Contributions to Causal Inference, Biostatistics, and Epidemiology, American Journal of Epidemiology , 193 (4), pp. 563-576.
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In the News

Why Women’s Networks Are Stronger During Crisis | Tiantian Yang

Professor Tiantian Yang explains why women stick together when facing disruption at work.…Read More

Knowledge @ Wharton - 2026/03/3
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Awards and Honors

IMS Medallion Lecturer 2022
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