arXiv 28 Oct 2023 · Statistics — Methodology · publishedJournal of the American Statistical Association (2025) · 1 citations (OpenAlex)
arXiv:2310.18836 · PDF · DOI · OpenAlex · Extracted main text
The literature on cluster-randomized trials typically allows for interference within but not across clusters. This may be implausible when units are irregularly distributed across space without well-separated communities, as clusters in such cases may not align with significant geographic, social, or economic divisions. This paper develops methods for reducing bias due to cross-cluster interference. We first propose an estimation strategy that excludes units not surrounded by clusters assigned to the same treatment arm. We show that this substantially reduces bias relative to conventional difference-in-means estimators without significant cost to variance. Second, we formally establish a bias-variance trade-off in the choice of clusters: constructing fewer, larger clusters reduces bias due to interference but increases variance. We provide a rule for choosing the number of clusters to balance the asymptotic orders of the bias and variance of our estimator. Finally, we consider unsupervised learning for cluster construction and provide theoretical guarantees for $k$-medoids.
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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 | Leung (2022) Rate-Optimal Cluster-Randomized Designs for Spatial Interference self | 1.000 | 10 | 6 | 100% |
| 2 | Hayes and Moulton (2017) | 1.000 | 6 | 3 | 100% |
| 3 | Hudgens and Halloran (2008) Toward Causal Inference with Interference | 1.000 | 6 | 3 | 100% |
| 4 | Cao, Hansen, Kozbur and Villacorta (2024) Inference for Dependent Data with Learned Clusters | 0.928 | 4 | 3 | 100% |
| 5 | Egger, Haushofer, Miguel, Niehaus and Walker (2022) General Equilibrium Effects of Cash Transfers: Experimental Evidence from Kenya | 0.874 | 14 | 2 | 100% |
| 6 | Baird, Bohren, McIntosh and Özler (2018) Optimal Design of Experiments in the Presence of Interference | 0.843 | 3 | 3 | 100% |
| 7 | Faridani and Niehaus (2024) Rate-Optimal Linear Estimation of Average Global Effects | 0.843 | 3 | 3 | 100% |
| 8 | Homan, Hiscox, Mweresa, Masiga, Mukabana et al (2016) The Effect of Mass Mosquito Trapping on Malaria Transmission and Disease Burden (SolarMal): A Stepped-Wedge Cluster-Randomised T… | 0.737 | 3 | 2 | 100% |
| 9 | Wainwright (2019) | 0.644 | 4 | 1 | 100% |
| 10 | Staples, Ogburn and Onnela (2015) Incorporating Contact Network Structure in Cluster Randomized Trials | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Linear estimation of global average treatment effects | 0.959 | 17 | 6 |