Davide Viviano, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi
arXiv 23 Oct 2023 · Econometrics · 6 citations (OpenAlex)
arXiv:2310.14983 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cai, J., A. D. Janvry, and E. Sadoulet (2015) Social networks and the decision to insure | 1.000 | 15 | 4 | 100% |
| 2 | Karrer, B., L. Shi, M. Bhole, M. Goldman, T. Palmer, C. Gelman, M. K… (2021) Network experimentation at scale self | 0.965 | 10 | 4 | 90% |
| 3 | Baird, S., J. A. Bohren, C. McIntosh, and B. Özler (2018) Optimal design of experiments in the presence of interference | 0.928 | 5 | 3 | 80% |
| 4 | Von Luxburg, U (2007) A tutorial on spectral clustering | 0.843 | 4 | 3 | 75% |
| 5 | Eckles, D., B. Karrer, and J. Ugander (2017) Design and analysis of experiments in networks: Reducing bias from interference | 0.737 | 3 | 2 | 100% |
| 6 | Toulis, P. and E. Kao (2013) Estimation of causal peer influence effects | 0.737 | 3 | 2 | 100% |
| 7 | Aronow, P. M. and C. Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.644 | 2 | 2 | 100% |
| 8 | Athey, S., D. Eckles, and G. W. Imbens (2018) Exact p-values for network interference | 0.644 | 2 | 2 | 100% |
| 9 | Breza, E., A. G. Chandrasekhar, T. H. McCormick, and M. Pan (2020) Using aggregated relational data to feasibly identify network structure without network data | 0.644 | 2 | 2 | 100% |
| 10 | Egger, D., J. Haushofer, E. Miguel, P. Niehaus, and M. Walker (2022) General equilibrium effects of cash transfers: experimental evidence from kenya | 0.644 | 2 | 2 | 100% |
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