Cristine Campos de Xavier Pinto
Escola de Economia de São Paulo, FGV/SP
Rua Itapeva 474, sala 1200
São Paulo– SP, Brasil, 01332-000
Institutional Affiliation: Escola de Economia de São Paulo
Information about this author at RePEc
NBER Working Papers and Publications
|November 2018||Semiparametrically Efficient Estimation of the Average Linear Regression Function|
with Bryan S. Graham: w25234
Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, and/or non-mutually exclusive “treatments”. Consider the linear regression of Y onto X in a subpopulation homogenous in W = w (formally a conditional linear predictor). Let b0 (w) be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average β0 = Ε[b0 (W)]. When X is binary-valued (multi-valued) our procedure recovers the (a vector of) average treatment effect(s). When X is continuously-valued, or consists of multiple non-exclusive treatments, our estimand coincides with the average partial effect (APE) of X on Y when the under...
|April 2011||Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)|
with Bryan S. Graham, Daniel Egel: w16928
We propose a locally efficient 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 estimator also possesses a double robustness property. Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, poverty maps, and semiparametric difference-in-differences. In an empirical application we use our procedure to characterize residual Black-White wage inequality after flexibly ...
Published: Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2016. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," Journal of Business & Economic Statistics, vol 34(2), pages 288-301. citation courtesy of
|May 2008||Inverse Probability Tilting for Moment Condition Models with Missing Data|
with Bryan S. Graham, Daniel Egel: w13981
We propose a new inverse probability weighting (IPW) estimator for moment condition models with missing data. Our estimator is easy to implement and compares favorably with existing IPW estimators, including augmented inverse probability weighting (AIPW) estimators, in terms of efficiency, robustness, and higher order bias. We illustrate our method with a study of the relationship between early Black-White differences in cognitive achievement and subsequent differences in adult earnings. In our dataset the early childhood achievement measure, the main regressor of interest, is missing for many units.
Published: Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079. citation courtesy of