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Finely Stratified Rerandomization Designs

Max Cytrynbaum

arXiv 3 Jul 2024 · Econometrics · 1 citations (OpenAlex)

arXiv:2407.03279 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We study estimation and inference on causal parameters under finely stratified rerandomization designs, which use baseline covariates to match units into groups (e.g. matched pairs), then rerandomize within-group treatment assignments until a balance criterion is satisfied. We show that finely stratified rerandomization does partially linear regression adjustment by design, providing nonparametric control over the stratified covariates and linear control over the rerandomized covariates. We introduce several new forms of rerandomization, allowing for imbalance metrics based on nonlinear estimators, and proposing a minimax scheme that minimizes the computational cost of rerandomization subject to a bound on estimation error. While the asymptotic distribution of GMM estimators under stratified rerandomization is generically non-normal, we show how to restore asymptotic normality using ex-post linear adjustment tailored to the stratification. We derive new variance bounds that enable conservative inference on finite population causal parameters, and provide asymptotically exact inference on their superpopulation counterparts.

Citation extraction

52
references
140
in-text mentions
52
distinct cited
3
self-citations
17,652
main-text words

appendix boundary found by appendix_titled_section at “Appendix” · 55% of the source is main text. Read the extracted text to check this.

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., Shaikh, A. M., and Tabord-Meehan, M (2024) On the efficiency of finely stratified experiments1.000104100%
2Bai, Y., Romano, J. P., and Shaikh, A. M (2021) Inference in experiments with matched pairs1.00085100%
3Cytrynbaum, M (2024) Covariate adjustment in stratified experiments self1.00063100%
4Wang, X., Wang, T., and Liu, H (2021) Rerandomization in stratified randomized experiments1.00053100%
5Angrist, J. D., Oreopoulos, P., and Williams, T (2013) New evidence on college achievement awards0.9619689%
6Bai, Y (2022) Optimality of matched-pair designs in randomized controlled trials0.92843100%
7Li, X., Ding, P., and Rubin, D. B (2018) Asymptotic theory of rerandomization in treatment–control experiments0.92843100%
8Wang, Y. and Li, X (2022) Rerandomization with diminishing covariate imbalance and diverging number of covariates0.84333100%
9Cytrynbaum, M (2024) Optimal stratification of survey experiments self0.77326746%
10Abadie, A., Imbens, G. W., and Zheng, F (2014) Inference for misspecified models with fixed inference for misspecified models with fixed regressors0.73732100%

Showing the top 10 of 52 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
1On the Efficiency of Highly Stratified Experiments0.87452
2A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances0.58531