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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.ME2023★ 1 cited
Transporting treatment effects from difference-in-differences studies
Audrey Renson, Ellicott C. Matthay, Kara E. Rudolph
Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample…