Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/320322 
Authors: 
Year of Publication: 
2024
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 15 [Issue:] 4 [Year:] 2024 [Pages:] 971-998
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
This paper studies covariate adjusted estimation of the average treatment effect in stratified experiments. We work in a general framework that includes matched tuples designs, coarse stratification, and complete randomization as special cases. Regression adjustment with treatment-covariate interactions is known to weakly improve efficiency for completely randomized designs. By contrast, we show that for stratified designs such regression estimators are generically inefficient, potentially even increasing estimator variance relative to the unadjusted benchmark. Motivated by this result, we derive the asymptotically optimal linear covariate adjustment for a given stratification. We construct several feasible estimators that implement this efficient adjustment in large samples. In the special case of matched pairs, for example, the regression including treatment, covariates, and pair fixed effects is asymptotically optimal. We also provide novel asymptotically exact inference methods that allow researchers to report smaller confidence intervals, fully reflecting the efficiency gains from both stratification and adjustment. Simulations and an empirical application demonstrate the value of our proposed methods.
Subjects: 
Matched pairs
analysis of covariance
blocking
robust standard er-ror
treatment effects
JEL: 
C10
C14
C90
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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