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March 2013 Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique
Winston Lin
Ann. Appl. Stat. 7(1): 295-318 (March 2013). DOI: 10.1214/12-AOAS583

Abstract

Freedman [Adv. in Appl. Math. 40 (2008) 180–193; Ann. Appl. Stat. 2 (2008) 176–196] critiqued ordinary least squares regression adjustment of estimated treatment effects in randomized experiments, using Neyman’s model for randomization inference. Contrary to conventional wisdom, he argued that adjustment can lead to worsened asymptotic precision, invalid measures of precision, and small-sample bias. This paper shows that in sufficiently large samples, those problems are either minor or easily fixed. OLS adjustment cannot hurt asymptotic precision when a full set of treatment–covariate interactions is included. Asymptotically valid confidence intervals can be constructed with the Huber–White sandwich standard error estimator. Checks on the asymptotic approximations are illustrated with data from Angrist, Lang, and Oreopoulos’s [Am. Econ. J.: Appl. Econ. 1:1 (2009) 136–163] evaluation of strategies to improve college students’ achievement. The strongest reasons to support Freedman’s preference for unadjusted estimates are transparency and the dangers of specification search.

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Winston Lin. "Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique." Ann. Appl. Stat. 7 (1) 295 - 318, March 2013. https://doi.org/10.1214/12-AOAS583

Information

Published: March 2013
First available in Project Euclid: 9 April 2013

zbMATH: 06171273
MathSciNet: MR3086420
Digital Object Identifier: 10.1214/12-AOAS583

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 1 • March 2013
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