Causal clustering: design of cluster experiments under network interference
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916585927606272 |
|---|---|
| author | Viviano, Davide Lei, Lihua Imbens, Guido Karrer, Brian Schrijvers, Okke Shi, Liang |
| author_facet | Viviano, Davide Lei, Lihua Imbens, Guido Karrer, Brian Schrijvers, Okke Shi, Liang |
| contents | This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_14983 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Causal clustering: design of cluster experiments under network interference Viviano, Davide Lei, Lihua Imbens, Guido Karrer, Brian Schrijvers, Okke Shi, Liang Econometrics Statistics Theory Methodology This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment. |
| title | Causal clustering: design of cluster experiments under network interference |
| topic | Econometrics Statistics Theory Methodology |
| url | https://arxiv.org/abs/2310.14983 |