arXiv 21 Jan 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2301.09016 · PDF · DOI · OpenAlex · Extracted main text
This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either control or treatment groups, based on individual-level covariates. Under the homogeneous partial interference assumption, I establish conditions under which the proposed difference-in-“average of averages” estimators are consistent and asymptotically normal for the corresponding average primary and spillover effects and develop consistent estimators of their asymptotic variances. Combining these results establishes the asymptotic validity of tests based on these estimators. My findings suggest that ignoring covariate information in the design stage can result in efficiency loss, and commonly used inference methods that ignore or improperly use covariate information can lead to either conservative or invalid inference. Then, I apply these results to studying optimal use of covariate information under covariate-adaptive randomization in large samples, and demonstrate that a specific generalized matched-pair design achieves minimum asymptotic variance for each proposed estimator. Finally, I discuss covariate adjustment, which incorporates additional baseline covariates not used for treatment assignment. The practical relevance of the theoretical results is illustrated through a simulation study and an empirical application.
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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 | Basse, G. and Feller, A (2018) Analyzing two-stage experiments in the presence of interference | 1.000 | 15 | 4 | 100% |
| 2 | Cytrynbaum, M (2023) Optimal stratification of survey experiments | 1.000 | 9 | 5 | 100% |
| 3 | Duflo, E. and Saez, E (2003) The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment* | 1.000 | 8 | 4 | 100% |
| 4 | Ichino, N. and Schündeln, M (2012) Deterring or displacing electoral irregularities? spillover effects of observers in a randomized field experiment in ghana | 1.000 | 7 | 4 | 100% |
| 5 | Tortarolo, D., Cruces, G. and Vazquez-Bare, G (2023) Design of partial population experiments with an application to spillovers in tax compliance | 1.000 | 7 | 3 | 100% |
| 6 | Bai, Y (2022) Optimality of matched-pair designs in randomized controlled trials | 0.969 | 11 | 5 | 91% |
| 7 | Foos, F. and de Rooij, E. A (2017) All in the family: Partisan disagreement and electoral mobilization in intimate networks—a spillover experiment | 0.965 | 10 | 4 | 90% |
| 8 | Bai, Y., Liu, J., Shaikh, A. M. and Tabord-Meehan, M (2022) Inference in cluster randomized trials with matched pairs self | 0.956 | 8 | 5 | 88% |
| 9 | Imai, K., Jiang, Z. and Malani, A (2021) Causal inference with interference and noncompliance in two-stage randomized experiments | 0.874 | 7 | 2 | 100% |
| 10 | Bai, Y., Jiang, L., Romano, J. P., Shaikh, A. M. and Zhang, Y (2023) Covariate adjustment in experiments with matched pairs | 0.874 | 6 | 3 | 67% |
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