Federico Bugni, Ivan Canay, Azeem Shaikh, Max Tabord-Meehan
arXiv 18 Apr 2022 · Econometrics · 6 citations (OpenAlex)
arXiv:2204.08356 · PDF · DOI · OpenAlex · Extracted main text
This paper considers the problem of inference in cluster randomized experiments when cluster sizes are non-ignorable. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the cluster level. By non-ignorable cluster sizes, we refer to the possibility that the treatment effects may depend non-trivially on the cluster sizes. We frame our analysis in a super-population framework in which cluster sizes are random. In this way, our analysis departs from earlier analyses of cluster randomized experiments in which cluster sizes are treated as non-random. We distinguish between two different parameters of interest: the equally-weighted cluster-level average treatment effect, and the size-weighted cluster-level average treatment effect. For each parameter, we provide methods for inference in an asymptotic framework where the number of clusters tends to infinity and treatment is assigned using a covariate-adaptive stratified randomization procedure. We additionally permit the experimenter to sample only a subset of the units within each cluster rather than the entire cluster and demonstrate the implications of such sampling for some commonly used estimators. A small simulation study and empirical demonstration show the practical relevance of our theoretical results.
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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 | Su, F. and P. Ding (2021) Model-assisted analyses of cluster-randomized experiments | 1.000 | 10 | 3 | 100% |
| 2 | Celhay, P. A., P. J. Gertler, P. Giovagnoli, and C. Vermeersch (2019) Long-run effects of temporary incentives on medical care productivity | 0.830 | 7 | 2 | 86% |
| 3 | Bugni, F. A., I. A. Canay, and A. M. Shaikh (2018) Inference under Covariate Adaptive Randomization self | 0.822 | 9 | 4 | 56% |
| 4 | Bai, Y., J. Liu, A. Shaikh, and M. Tabord-Meehan (2022) Inference for Cluster Randomized Experiments with Matched Pairs, Working paper | 0.737 | 3 | 3 | 67% |
| 5 | Hansen, B. and S. Lee (2019) Asymptotic theory for clustered samples | 0.737 | 3 | 2 | 100% |
| 6 | Athey, S. and G. W. Imbens (2017) The econometrics of randomized experiments, in | 0.644 | 4 | 1 | 100% |
| 7 | Liang, K.-Y. and S. L. Zeger (1986) Longitudinal data analysis using generalized linear models | 0.644 | 2 | 2 | 100% |
| 8 | Bugni, F. A., I. A. Canay, and A. M. Shaikh (2019) Inference under Covariate-Adaptive Randomization with Multiple Treatments self | 0.630 | 8 | 3 | 25% |
| 9 | Liu, J (2023) Inference for Two-stage Experiments under Covariate-Adaptive Randomization | 0.511 | 2 | 2 | 50% |
| 10 | Duflo, E., R. Glennerster, and M. Kremer (2007) Using randomization in development economics research: A toolkit | 0.511 | 2 | 1 | 100% |
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