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

Links: Personal Website

Overview

Education

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

Academic Positions Held

Wharton: 2002-present

For more information, go to My Personal Page

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Research

Teaching

All Courses

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • BSTA6990 - Lab Rotation

  • BSTA9200 - Tutorial: Research

  • BSTA9950 - Dissertation

  • STAT1010 - Intro Business Stat

    Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT3990 - Independent Study

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

  • STAT4750 - Sample Survey Design

    This course will cover the design and analysis of sample surveys. Topics include simple sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT4900 - 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. The course is intended for statistics concentrators and minors. Knowledge of R such as that covered in STAT 4050 or STAT 4700 is recommended.

  • 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.

  • STAT9200 - Sample Survey Methods

    This course will cover the design and analysis of sample surveys. Topics include simple random sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias.

  • 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.

  • STAT9700 - Mathematical Statistics

    Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.

  • 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 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.

  • STAT9950 - Dissertation

  • STAT9990 - 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 at Wharton

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Activity

Latest Research

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
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In the News

Sports and Statistics: Correlating Football to Brain Injury

New Wharton research examines the long-term impact of playing high school or college football.Read More

Knowledge at Wharton - 7/21/2017
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Wharton Magazine

All Over the Map

How Wharton’s research programs prepare undergraduates for careers in academia and the private sector.

Wharton Magazine - 01/01/2011

Wharton Stories

PhiladelphiaHow to Use Big Data to Make Cities Safer

How can we use publicly available data to understand what makes a city neighborhood safe? To answer this question, Shane Jensen and Dylan Small, Professors in Wharton’s Statistics Department, are using their skills in big data to reveal patterns of crime and safety in the city. Jonathan Wood, a WSII…

Wharton Stories - 04/09/2018
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