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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…