TY  - JOUR
AU  - Imbens,Guido W.
TI  - Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 294
PY  - 2003
Y2  - October 2003
UR  - http://www.nber.org/papers/t0294
L1  - http://www.nber.org/papers/t0294.pdf
N1  - Author contact info:
Guido Imbens
Graduate School of Business
Stanford University
655 Knight Way
Stanford, CA 94305
Tel: 617/384-7485
Fax: 617/495-7730
E-Mail: Imbens@stanford.edu
AB  - Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (e.g., average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functional form assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, methods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this literature and discuss some of its unanswered questions, focusing in particular on the practical implementation of these methods, the plausibility of this exogeneity assumption in economic applications, the relative performance of the various semiparametric estimators when the key assumptions (unconfoundedness and overlap) are satisfied, alternative estimands such as quantile treatment effects, and alternate methods such as Bayesian inference.
ER  -