Structural Nested Mean Models Under Parallel Trends with Interference
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912678569574400 |
|---|---|
| author | Shahn, Zach Zivich, Paul Renson, Audrey |
| author_facet | Shahn, Zach Zivich, Paul Renson, Audrey |
| contents | Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparatively little work has considered how to accommodate and learn about spillover effects within a DiD framework. Here, we extend the `DiD-SNMMs' of Shahn et al (2022) to accommodate interference in a time-varying DiD setting. Doing so enables estimation of a richer set of effects than previous DiD approaches. For example, DiD-SNMMs do not assume the absence of spillover effects after direct exposures and can model how effects of direct or indirect (i.e. spillover) exposures depend on past and concurrent (direct or indirect) exposure and covariate history. We consider both cluster and network interference structures and illustrate the methodology in simulations and an application to effects of Medicaid expansion on uninsurance rates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Structural Nested Mean Models Under Parallel Trends with Interference Shahn, Zach Zivich, Paul Renson, Audrey Methodology Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparatively little work has considered how to accommodate and learn about spillover effects within a DiD framework. Here, we extend the `DiD-SNMMs' of Shahn et al (2022) to accommodate interference in a time-varying DiD setting. Doing so enables estimation of a richer set of effects than previous DiD approaches. For example, DiD-SNMMs do not assume the absence of spillover effects after direct exposures and can model how effects of direct or indirect (i.e. spillover) exposures depend on past and concurrent (direct or indirect) exposure and covariate history. We consider both cluster and network interference structures and illustrate the methodology in simulations and an application to effects of Medicaid expansion on uninsurance rates. |
| title | Structural Nested Mean Models Under Parallel Trends with Interference |
| topic | Methodology |
| url | https://arxiv.org/abs/2405.11781 |