Jiawei Fu, Cyrus Samii, Ye Wang
arXiv 2 Jul 2026 · Statistics — Methodology
arXiv:2607.02385 · PDF · DOI · OpenAlex · Extracted main text
A common experimental research design is one in which individuals are randomly allocated into groups that then interact under different group-level treatment conditions. We develop design-based inference for such "group interaction" experiments, covering scenarios in which groups are either fixed or randomly formed and in which potential outcomes are either fixed relative to others' group assignments or subject to interference. For each scenario, we characterize the causal estimand that the design targets and the inferential strategy appropriate to it. Working in a sparse-sampling asymptotic regime, we show that cluster-robust inference remains consistent and accounts for dependencies from various sources when interference is present, delivering valid inference on marginalized exposure effects. When interference is absent and groups are formed randomly, the design reduces to an individually randomized experiment, and individual-level heteroskedasticity-robust inference suffices for the average treatment effect. Our results on the asymptotic distribution of commonly used estimators rely on a novel coupling strategy that may be useful for design-based inference in other complex experiments.
appendix boundary found by appendix_titled_section at “Appendix” · 45% of the source is main text. Read the extracted text to check this.
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 | Iacovone, Leonardo and Maloney, William and McKenzie, David (2022) Improving management with individual and group-based consulting: Results from a randomized experiment in Colombia | 1.000 | 10 | 4 | 100% |
| 2 | Mendelberg, Tali and Karpowitz, Christopher F and Goedert, Nicholas (2014) Does descriptive representation facilitate women's distinctive voice? How gender composition and decision rules affect deliberat… | 0.941 | 12 | 5 | 83% |
| 3 | Abadie, Alberto and Athey, Susan and Imbens, Guido W and Wooldridge,… (2023) When should you adjust standard errors for clustering? | 0.928 | 4 | 3 | 100% |
| 4 | Hudgens, Michael G and Halloran, M Elizabeth (2008) Toward causal inference with interference | 0.928 | 4 | 3 | 100% |
| 5 | Su, Fangzhou and Ding, Peng (2021) Model-assisted analyses of cluster-randomized experiments | 0.843 | 4 | 4 | 75% |
| 6 | Imbens, Guido W and Rubin, Donald B (2015) Causal inference in statistics, social, and biomedical sciences | 0.843 | 3 | 3 | 100% |
| 7 | Ohlsson, Esbjörn (1989) Asymptotic normality for two-stage sampling from a finite population | 0.737 | 4 | 3 | 50% |
| 8 | Lin, Winston (2013) Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique | 0.737 | 3 | 3 | 67% |
| 9 | Li, Xinran and Ding, Peng and Lin, Qian and Yang, Dawei and Liu, Jun S (2019) Randomization Inference for Peer Effects | 0.737 | 3 | 2 | 100% |
| 10 | Bai, Yuehao (2022) Optimality of matched-pair designs in randomized controlled trials | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study | 0.405 | 1 | 1 |