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

Cyrus Samii; Peter M. Aronow

doi:10.1016/j.spl.2011.10.024

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

Cyrus Samii, Peter M. Aronow

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	365-370
Number of pages	6
Journal	Statistics and Probability Letters
Volume	82
Issue number	2
DOIs	https://doi.org/10.1016/j.spl.2011.10.024
State	Published - Feb 2012

Keywords

Potential outcomes
Randomized experiments
Robust variance estimators

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/j.spl.2011.10.024

Cite this

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

Abstract

Keywords

ASJC Scopus subject areas

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