Dylan Small

• Kwonsang Lee, Bhaswar B. Bhattacharya, Jing Qin, Dylan Small (2023), A nonparametric binomial likelihood approach for causal inference in instrumental variable models, Journal of the Korean Statistical Society, 52 (), pp. 1055-1077.
• 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.

  Related
  Links
• Bikram Karmakar and Dylan Small (2020), Assessment of the Extent of Corroboration of an Elaborate Theory of a Causal Hypothesis Using Partial Conjunctions of Evidence Factors, Annals of Statistics, (to appear) ().
• Hyunseung Kang, Tony Cai, Dylan Small (Under Review), Robust Confidence Intervals for Causal Effects with Possibly Invalid Instruments.
• Qingyuan Zhao, Jingshu Wang, Gibran Hemani, Jack Bowden, Dylan Small (2020), Statistical Inference in Two-sample Summary-data Mendelian Randomization using Robust Adjusted Profile Score, Annals of Statistics, (in press) ().
• Bo Zhang, Jordan Weiss, Dylan Small, Qingyuan Zhao (2020), Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding, Journal of the American Statistical Association, (to appear) ().
• Bikram Karmakar, Chyke A. Doubeni, Dylan Small (2020), Evidence Factors in a Case-control Study with Application to the Effect of Flexible Sigmoidoscopy Screening on Colorectal Cancer, Annals of Applied Statistics, (to appear) ().
• Edward H. Kennedy and Dylan Small (2020), Paradoxes in Instrumental Variable Studies with Missing Data and One-sided Noncompliance, Journal of the French Statistical Society, (in press) ().
• Timothy G. Gaulton, Sameer K. Deshpande, Dylan Small, Mark D. Neuman (2020), Observational Study of the Association between Participation in High School Football and Self-Rated Health, Obesity, and Pain in Adulthood, American Journal of Epidemiology, (to appear) ().
• Colman Humphrey, Shane T. Jensen, Dylan Small, Rachel Thurston (2020), Analysis of urban vibrancy and safety in Philadelphia, Environment and Planning B: Urban Analytics and City Science, 47 (), pp. 1573-1587.
• 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.

• 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.

• 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.

• 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

  Dissertation

• STAT9990 - 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

• How Dynamic Electricity Pricing Can Improve Market Efficiency, Knowledge @ Wharton - 11/12/2024
• Is Algorithmic Management Too Controlling?, Knowledge @ Wharton - 11/12/2024
• The Hidden Cost of AI Energy Consumption, Knowledge @ Wharton - 11/12/2024
• Charitable and Political Giving | Pinar Yildirim, Knowledge @ Wharton - 11/12/2024
• The Future of Banking, Knowledge @ Wharton - 11/11/2024
• Election Statistics, Knowledge @ Wharton - 11/06/2024
• Robots Are Taking Over Low-skilled Jobs — and Changing Votes, Knowledge @ Wharton - 11/05/2024
• Crypto Politics: Liberals’ and Conservatives’ Confidence in Cryptocurrencies, Knowledge @ Wharton - 11/05/2024
• Leadership and Elections | Mike Useem, Knowledge @ Wharton - 11/05/2024
• Automation Is Harming Low-skilled Workers and Possibly Their Voting Preferences, Knowledge @ Wharton - 11/01/2024