Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman’s Critique By
نویسندگان
چکیده
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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“ Agnostic Notes on Regression Adjustments to Experimental Data : Reexamining
• Section 2 defines the basic notation; • Section 4.2.1 states Conditions 1–3; • Section 4.2.2 defines the vectors Qa and Qb and the prediction errors ai and bi , and introduces the σx and σx,y notation for population variances and covariances; • Section 5 defines the vector Q and the prediction errors a∗∗ i and b∗∗ i . Let p̃A = nA/n [as in remark (iii) after Corollary 1.2]. Extend Section 2’s ...
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