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Efficient estimation of data combination models by the method of auxiliary-to-study tilting (AST)

Author: Bryan S Graham; Cristine Campos de Xavier Pinto; Daniel Egel; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, ©2011.
Series: Working paper series (National Bureau of Economic Research), no. 16928.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
We propose a locally efficient, doubly robust, estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems analyzed by Robins, Rotnitzky and Zhao (1994) (of which the Average Treatment Effect (ATE) estimand is a special case).  Read more...
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Additional Physical Format: Print version:
Graham, Bryan S.
Efficient estimation of data combination models by the method of auxiliary-to-study tilting (AST).
Cambridge, Mass. : National Bureau of Economic Research, ©2011
(DLC) 2011657160
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Bryan S Graham; Cristine Campos de Xavier Pinto; Daniel Egel; National Bureau of Economic Research.
OCLC Number: 712022555
Notes: "April 2011."
Title from http://www.nber.org/papers/16928 viewed Apr. 11, 2011.
Has online supplemental appendix which " ... contains a proof of Theorem 2.1. It also details some of the more tedious calculations underlying the proof to Theorem 3.1. All notation is as defined in the main text unless stated otherwise. Equation and Table numbering continues in sequence with that established in the main text."
Description: 1 online resource (44, 12 pages) : illustrations.
Series Title: Working paper series (National Bureau of Economic Research), no. 16928.
Responsibility: Bryan S. Graham, Cristine Campos de Xavier Pinto, Daniel Egel.

Abstract:

We propose a locally efficient, doubly robust, estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems analyzed by Robins, Rotnitzky and Zhao (1994) (of which the Average Treatment Effect (ATE) estimand is a special case). Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, and poverty maps. In an empirical application we use our procedure to characterize residual Black-White wage inequality after flexibly controlling for 'pre-market' differences in measured cognitive achievement as in Neal and Johnson (1996). We find that residual Black-White inequality is negligible at lower and higher quantiles of the Black wage distribution, but substantial at middle quantiles.

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