Working Paper: NBER ID: w16928
Authors: Bryan S. Graham; Cristine Campos de Xavier Pinto; Daniel Egel
Abstract: 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 controlling for 'pre-market' differences in measured cognitive achievement as in Neal and Johnson (1996).
Keywords: data combination; average treatment effect; semiparametric models; double robustness
JEL Codes: C01; C14; J31; J7
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AST estimator (C51) | average treatment effect on the treated (ATT) (C22) |
| AST estimator (C51) | efficiency in specific contexts (D61) |
| overlap between study and auxiliary populations (C34) | effectiveness of AST estimator (C51) |
| model misspecifications (C52) | consistency of AST estimator (C51) |