On equivalencies between design-based and regression-based variance estimators for randomized experiments

Cyrus Samii, Peter M. Aronow

    Research output: Contribution to journalArticle

    Abstract

    This paper demonstrates that the randomization-based "Neyman" and constant-effects estimators for the variance of estimated average treatment effects are equivalent to a variant of the White "heteroskedasticity-robust" estimator and the homoskedastic ordinary least squares (OLS) estimator, respectively.

    Original languageEnglish (US)
    Pages (from-to)365-370
    Number of pages6
    JournalStatistics and Probability Letters
    Volume82
    Issue number2
    DOIs
    StatePublished - Feb 1 2012

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    Keywords

    • Potential outcomes
    • Randomized experiments
    • Robust variance estimators

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty

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