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.
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) ().
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.
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.
Uniting Great Minds, Wharton’s Stat Bridge MA Program Takes Flight