Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review

49 Pages Posted: 25 Apr 2011 Last revised: 23 Apr 2023

See all articles by Guido W. Imbens

Guido W. Imbens

Stanford Graduate School of Business

Date Written: October 2003

Abstract

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.

Suggested Citation

Imbens, Guido W., Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review (October 2003). NBER Working Paper No. t0294, Available at SSRN: https://ssrn.com/abstract=1820074

Guido W. Imbens (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
51
Abstract Views
8,485
Rank
690,560
PlumX Metrics