Multiple Randomization Designs: Estimation and Inference with Interference
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912744358281216 |
|---|---|
| author | Masoero, Lorenzo Vijaykumar, Suhas Richardson, Thomas McQueen, James Rosen, Ido Burdick, Brian Bajari, Pat Imbens, Guido |
| author_facet | Masoero, Lorenzo Vijaykumar, Suhas Richardson, Thomas McQueen, James Rosen, Ido Burdick, Brian Bajari, Pat Imbens, Guido |
| contents | Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limited value for answering standard questions in settings, common in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this paper, we discuss new experimental designs and corresponding estimands to account for and capture these complex spillovers. We derive the finite-sample properties of tractable estimators for main effects, direct effects, and spillovers, and present associated central limit theorems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01264 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiple Randomization Designs: Estimation and Inference with Interference Masoero, Lorenzo Vijaykumar, Suhas Richardson, Thomas McQueen, James Rosen, Ido Burdick, Brian Bajari, Pat Imbens, Guido Methodology Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limited value for answering standard questions in settings, common in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this paper, we discuss new experimental designs and corresponding estimands to account for and capture these complex spillovers. We derive the finite-sample properties of tractable estimators for main effects, direct effects, and spillovers, and present associated central limit theorems. |
| title | Multiple Randomization Designs: Estimation and Inference with Interference |
| topic | Methodology |
| url | https://arxiv.org/abs/2401.01264 |