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Optimal Stratification of Survey Experiments

Max Cytrynbaum

arXiv 16 Nov 2021 · Econometrics · 6 citations (OpenAlex)

arXiv:2111.08157 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies a two-stage model of experimentation, where the researcher first samples representative units from an eligible pool, then assigns each sampled unit to treatment or control. To implement balanced sampling and assignment, we introduce a new family of finely stratified designs that generalize matched pairs randomization to propensities p(x) not equal to 1/2. We show that two-stage stratification nonparametrically dampens the variance of treatment effect estimation. We formulate and solve the optimal stratification problem with heterogeneous costs and fixed budget, providing simple heuristics for the optimal design. In settings with pilot data, we show that implementing a consistent estimate of this design is also efficient, minimizing asymptotic variance subject to the budget constraint. We also provide new asymptotically exact inference methods, allowing experimenters to fully exploit the efficiency gains from both stratified sampling and assignment. An application to nine papers recently published in top economics journals demonstrates the value of our methods.

Citation extraction

51
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Bai, Y (2022) Optimality of matched-pair designs in randomized controlled trials1.000134100%
2Hahn, J (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects1.00054100%
3Bai, Y., J. P. Romano, and A. M. Shaikh (2021) Inference in experiments with matched pairs0.9568588%
4Fan, J. and Q. Yao (1998) Efficient estimation of conditional variance functions in stochastic regression0.8434475%
5Baysan, C (2022) Persistent polarizing effects of persuasion: Experimental evidence from turkey0.8434375%
6Domurat, R., I. Menashe, and W. Yin (2021) The role of behavioral frictions in health insurance marketplace enrollment and risk: Evidence from a field experimen0.8434375%
7Finkelstein, A., S. Taubman, B. Wright, M. Bernstein, J. Gruber, J.… (2012) The oregon health insurance experiment: Evidence from the first year0.8115280%
8Tabord-Meehan, M (2022) Stratification trees for adaptive randomisation in randomised controlled trials0.81142100%
9Abadie, A. and G. W. Imbens (2008) Estimation of the conditional variance in paired experiments0.73732100%
10Hahn, J., K. Hirano, and D. Karlan (2011) Adaptive experimental design using the propensity score0.73732100%

Showing the top 10 of 51 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Inference for Two-stage Experiments under Covariate-Adaptive Randomization1.00095
2On the Performance of the Neyman Allocation with Small Pilots0.81142
3Finely Stratified Rerandomization Designs0.773267
4Covariate Adjustment in Stratified Experiments0.705206
5A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances0.64422
6A New Design-Based Variance Estimator for Finely Stratified Experiments0.64422
7Coupling Designs for Randomized Experiments with Complex Treatments0.64422
8Adjustments with Many Regressors under Covariate-Adaptive Randomizations0.51121
9A Combinatorial Central Limit Theorem for Stratified Randomization0.40511
10Randomization Inference: Theory and Applications0.40511