Dylan Small

• 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.
• Kwonsang Lee, Bhaswar B. Bhattacharya, Jing Qin, Dylan Small (2023), A Nonparametric Likelihood Approach for Inference in Instrumental Variable Models, Journal of the Korean Statistical Society , 52 (p.p. 1055-1077).
• Jeffrey Zhang and Dylan Small (2023), Sensitivity Analysis for Observational Studies with Recurrent Events, Lifetime Data Analysis, 30 (p.p. 237-261).
• Xinran Li and Dylan Small (2023), Randomization-Based Test for Censored Outcomes: A new Look at the Logrank Test, Statistical Science, 38 (1), pp. 92-107.
• 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.
• Jing Cheng and Dylan Small (2021), Semiparametric Models and Inference for the Effect of a Treatment When the Outcome is Nonnegative with Clumping at Zero, Biometrics, 77 (4), pp. 1187-1201.
• Siyu Heng and Dylan Small (2021), Sharpening the Rosenbaum Sensitivity Bounds to Address Concerns about Interactions Between Observed and Unobserved Covariates, Statistica Sinica, 31 ().
• Halima Twabi, Samuel Manda, Dylan Small (2021), Evaluating the Causal Effect of Appropriate Complementary Feeding on Child Growth in Malawi Using Cross-Sectional Data: An Application of Propensity Score Matching, Frontiers in Nutrition , 8 ().
• Srinath Adusumalli, Julie Westover, Douglas Jacoby, Dylan Small, Christine VanZandbergen, Jessica Chen, Ann Cavella, Rebecca Pepe, Charles Rareshide, Christopher Snider, Kevin Volpp, David A. Asch, Mitesh Patel (2021), Effect of Passive Choice and Active Choice Interventions in the Electronic Health Record to Cardiologists on Stain Prescribing: A Cluster Randomized Clinical Trial, JAMA Cardiology, 6 (p.p. 40-48).
• Rinad S. Beidas, Brian Ahmedani, Kristin Linn, Steven C. Marcus, Christina Johnson, Melissa Maye, Joslyn Westphal, Leslie Wright, Arne Beck, Alison Buttenheim, Matthew Daley, Molly Davis, Marisa Elias, Shari Jager-Hyman, Katelin Hoskins, Adina Lieberman, Bridget McArdle, Debra Ritzwoller, Dylan Small, Courtney Benjamin Wolk, Nathaniel Williams, Jennifer Boggs (2021), Study protocol for a Type III hybrid effectiveness-implementation trial of strategies to implement firearm safety promotion as a universal suicide prevention strategy in pediatric primary care, Implementation Science , 16 (), pp. 1-16.
• All Research from Dylan Small »

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.

• BSTA9950 - Dissertation

  Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.

• 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.

• 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.

• 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 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.

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

Knowledge @ Wharton

• Why All Investors Are Vulnerable to ‘Correlation Neglect’, Knowledge @ Wharton - 02/10/2026
• Why Some Founders in Startup Accelerators Do Better Than Others, Knowledge @ Wharton - 02/10/2026
• Hand Gestures Can Help You Sell. Here’s Why, Knowledge @ Wharton - 02/10/2026
• Future of Finance | Joao Gomes and Itay Goldstein, Knowledge @ Wharton - 02/10/2026
• The Economic Reality Behind Billionaires’ Taxes and State Budgets, Knowledge @ Wharton - 02/06/2026
• Ad Age Editor-in-Chief, Jeanine Poggi, Knowledge @ Wharton - 02/05/2026
• How Advertising Could Reshape ChatGPT and Digital Marketing, Knowledge @ Wharton - 02/04/2026
• Scaling Insights: How Big Data and Simulation Are Transforming the NFL, Knowledge @ Wharton - 02/04/2026
• How Investor Wealth Grows With Delaware’s Politically Balanced Judiciary, Knowledge @ Wharton - 02/03/2026
• Your Organization’s Unwritten Rules and How to Fix Them, Knowledge @ Wharton - 02/03/2026