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| Format: | Artículo Open Access |
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Wiley
2025
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/sim.70139 |
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Table of Contents:
- Sharp Variance Estimator and Causal Bootstrap in Stratified Randomized Experiments Haoyang Yu Ke Zhu Hanzhong Liu Statistics in Medicine ABSTRACTRandomized experiments are the gold standard for estimating treatment effects, and randomization serves as a reasoned basis for inference. In widely used stratified randomized experiments, randomization‐based finite‐population asymptotic theory enables valid inference for the average treatment effect, relying on normal approximation and a Neyman‐type conservative variance estimator. However, when the sample size is small or the outcomes are skewed, the Neyman‐type variance estimator may become overly conservative, and the normal approximation can fail. To address these issues, we propose a sharp variance estimator and two causal bootstrap methods to more accurately approximate the sampling distribution of the weighted difference‐in‐means estimator in stratified randomized experiments. The first causal bootstrap procedure is based on rank‐preserving imputation, and we prove its second‐order refinement over normal approximation. The second causal bootstrap procedure is based on constant‐treatment‐effect imputation and is further applicable in paired experiments. In contrast to traditional bootstrap methods, where randomness originates from hypothetical super‐population sampling, our analysis for the proposed causal bootstrap is randomization‐based, relying solely on the randomness of treatment assignment in randomized experiments. Numerical studies and two real data applications demonstrate the advantages of our proposed methods in finite samples. The R package CausalBootstrap implementing our method is publicly available. 10.1002/sim.70139 http://onlinelibrary.wiley.com/termsAndConditions#vor