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stat.ME2024
Generalizing Difference-in-Differences to Non-Canonical Settings: Identifying an Array of Estimands
Zach Shahn, Laura Hatfield
Consider a general setting in which data on an outcome is collected in two `groups' at two time periods, with certain group-periods deemed `treated' and others `untreated'. A speci…
stat.ME2024
Structural Nested Mean Models Under Parallel Trends with Interference
Zach Shahn, Paul Zivich, Audrey Renson
Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparati…
stat.ME2024
Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations
Shenbo Xu, Raluca Cobzaru, Stan N. Finkelstein +3
Methods for estimating heterogeneous treatment effects (HTE) from observational data have largely focused on continuous or binary outcomes, with less attention paid to survival out…