3 papers
stat.ME2025
A Universal Framework for Factorial Matched Observational Studies with General Treatment Types: Design, Analysis, and Applications
Jianan Zhu, Tianruo Zhang, Diana Silver +4
Matching is one of the most widely used causal inference frameworks in observational studies. However, all the existing matching-based causal inference methods are designed for eit…
stat.ME2025
A Non-Bipartite Matching Framework for Difference-in-Differences with General Treatment Types
Siyu Heng, Yuan Huang, Hyunseung Kang
Difference-in-differences (DID) is one of the most widely used causal inference frameworks in observational studies. However, most existing DID methods are designed for binary trea…
stat.ME2025
Exact, Nonparametric Sensitivity Analysis for Observational Studies of Contingency Tables
Elaine K. Chiu, Hyunseung Kang
In observational studies, contingency tables are commonly used to examine associations between categorical variables. However, any test of association in contingency tables may be…