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Preprints, Working Papers, ... Year : 2024

A split-treatment design


I devise a difference-in-differences design that accounts for the possibility that some treatment effect is split in the reactions to two or more events. At the intersection of settings with a single treatment and with multiple treatments, regression-based methods for this split-treatment design can be subject both to negative weights and contamination bias. I propose a simple solution, a first-difference regression with sample constraints in the spirit of Dube et al.’s (2023) LP-DiD, that allows to identify and estimate sensible causal parameters of interest. This estimator is efficient under random walk errors and unrestricted heterogeneity across groups and events.
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hal-04532056 , version 1 (04-04-2024)


  • HAL Id : hal-04532056 , version 1


Jean-Baptiste Bonnier. A split-treatment design. 2024. ⟨hal-04532056⟩


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