Finely Stratified Rerandomization Designs

[2407.03279] Finely Stratified Rerandomization Designs Skip to main content Search Submit Donate Log in Economics > Econometrics arXiv:2407.03279 (econ) [Submitted on 3 Jul 2024 ( v1 ), last revised 26 Aug 2026 (this version, v4)] Title: Finely Stratified Rerandomization Designs Authors: Max Cytrynbaum View PDF HTML (experimental) Abstract: Finely stratified randomization makes unadjusted treatment effect estimation efficient when matches are tight. However, match quality deteriorates rapidly as covariate dimension increases, attenuating the gains from stratification. Motivated by this problem, we study designs that finely stratify on a few important covariates, then rerandomize within matched groups to balance the remainder. We derive the asymptotic distribution for GMM estimators of general causal parameters under such designs, showing that they provide nonparametric control over the stratification covariates and linear control over the rerandomization covariates. The resulting distribution is generally non-normal, but optimal linear adjustment restores asymptotic normality. For finite population parameters, we derive upper bounds on the non-identified asymptotic variance, enabling conservative inference that accounts for the efficiency gains from both design stages. An empirical application to estimating treatment effect heterogeneity among compliers illustrates the gains from adding rerandomization to a stratified design. Subjects: Econometrics (econ.EM) ; Statistics Theory (math.ST) Cite as: arXiv:2407.03279 [econ.EM] (or arXiv:2407.03279v4 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2407.03279 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Max Cytrynbaum [ view email ] [v1] Wed, 3 Jul 2024 17:04:44 UTC (83 KB) [v2] Sun, 28 Jul 2024 14:06:11 UTC (147 KB) [v3] Sat, 4 Jan 2025 20:25:02 UTC (435 KB) [v4] Wed, 26 Aug 2026 19:30:00 UTC (207 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2024-07 Change to browse by: econ math math.ST stat stat.TH References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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