A New Design-Based Variance Estimator for Finely Stratified Experiments

Yuehao Bai, Xun Huang, Joseph P. Romano, Azeem M. Shaikh and Max Tabord-Meehan

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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

Date: 2025-03
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