Research
Exact, Nonparametric Sensitivity Analysis for Observational Studies of Contingency Tables
This paper develops the first exact, nonparametric sensitivity analysis framework for multi-category contingency tables, providing valid inference under unmeasured confounding. It generalizes Rosenbaum’s generic sensitivity analysis to general I×J treatment–outcome structures, offering both exact and asymptotic inference methods.
Towards Robust Matched Observational Studies with General Treatment Types: Consistency, Efficiency, and Adaptivity
This project extends sensitivity analysis frameworks to matched observational studies with continuous treatments, investigating optimal test statistics through design sensitivity and Bahadur relative efficiency.
Sensitivity Analysis for Matched-Pair Cluster Randomization Designs
This work investigates optimal test statistics under matched-pair cluster randomization designs and derives sensitivity values and Bahadur relative efficiencies.