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A New Design-Based Variance Estimator for Finely Stratified Experiments

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Listed:
  • Yuehao Bai
  • Xun Huang
  • Joseph P. Romano
  • Azeem M. Shaikh
  • Max Tabord-Meehan

Abstract

This paper considers the problem of design-based inference for the average treatment effect in finely stratified experiments. Here, by "design-based'' we mean that the only source of uncertainty stems from the randomness in treatment assignment; by "finely stratified'' we mean units are first stratified into groups of size k according to baseline covariates and then, within each group, a fixed number l

Suggested Citation

  • Yuehao Bai & Xun Huang & Joseph P. Romano & Azeem M. Shaikh & Max Tabord-Meehan, 2025. "A New Design-Based Variance Estimator for Finely Stratified Experiments," Papers 2503.10851, arXiv.org.
    • Handle: RePEc:arx:papers:2503.10851
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    File URL: http://arxiv.org/pdf/2503.10851
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