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On the Performance of the Neyman Allocation with Small Pilots

Yong Cai, Ahnaf Rafi

arXiv 9 Jun 2022 · Econometrics · publishedJournal of Econometrics (2024) · 2 citations (OpenAlex)

arXiv:2206.04643 · PDF · DOI · OpenAlex · Extracted main text

Abstract

The Neyman Allocation is used in many papers on experimental design, which typically assume that researchers have access to large pilot studies. This may be unrealistic. To understand the properties of the Neyman Allocation with small pilots, we study its behavior in an asymptotic framework that takes pilot size to be fixed even as the size of the main wave tends to infinity. Our analysis shows that the Neyman Allocation can lead to estimates of the ATE with higher asymptotic variance than with (non-adaptive) balanced randomization. In particular, this happens when the outcome variable is relatively homoskedastic with respect to treatment status or when it exhibits high kurtosis. We provide a series of empirical examples showing that such situations can arise in practice. Our results suggest that researchers with small pilots should not use the Neyman Allocation if they believe that outcomes are homoskedastic or heavy-tailed. Finally, we examine some potential methods for improving the finite sample performance of the FNA via simulations.

Citation extraction

36
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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
1Cytrynbaum, M (2021) Designing representative and balanced experiments by local randomization0.87482100%
2Hahn, J., K. Hirano, and D. Karlan (2011) Adaptive experimental design using the propensity score0.87452100%
3Cytrynbaum, M (2023) Optimal stratification of survey experiments0.81142100%
4Ashraf, N., D. Karlan, and W. Yin (2006) Tying odysseus to the mast: Evidence from a commitment savings product in the philippines0.69391100%
5Avvisati, F., M. Gurgand, N. Guyon, and E. Maurin (2014) Getting parents involved: A field experiment in deprived schools0.69371100%
6Blackwell, M., N. E. Pashley, and D. Valentino (2022) Batch adaptive designs to improve efficiency in social science experiments0.64422100%
7Bai, Y (2022) Optimality of matched-pair designs in randomized controlled trials0.58531100%
8Tabord-Meehan, M (2021) Stratification trees for adaptive randomization in randomized controlled trials0.51121100%
9Canay, I. A. and V. Kamat (2017, 10) (2017) Approximate Permutation Tests and Induced Order Statistics in the Regression Discontinuity Design0.40511100%
10Azriel, D., M. Mandel, and Y. Rinott (2012) Optimal allocation to maximize the power of two-sample tests for binary response0.40511100%

Showing the top 10 of 36 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
12.5cm When and How to Pilot: Design Rules for Two-Wave Experiments0.84343
2Efficient Adaptive Experimental Design for Average Treatment Effect Estimation0.51121
3Optimal Stratification of Survey Experiments0.40511
4On the Efficiency of Highly Stratified Experiments0.40511
5Causal clustering: design of cluster experiments under network interference0.40511
6A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances0.40511
7Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification0.40511
8Coupling Designs for Randomized Experiments with Complex Treatments0.00011