  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.
