Research

Exact, Nonparametric Sensitivity Analysis for Observational Studies of Contingency Tables

arXiv:2507.17207
Elaine K. Chiu and Hyunseung Kang.

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

arXiv:2403.14152
Siyu Heng, Elaine K. Chiu, and Hyunseung Kang.

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

Elaine K. Chiu and Hyunseung Kang. In preparation.

This work investigates optimal test statistics under matched-pair cluster randomization designs and derives sensitivity values and Bahadur relative efficiencies.