Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges
- (pp. 278-81)
Abstract
There is a large literature on semiparametric estimation of average treatment effects under unconfounded treatment assignment in settings with a fixed number of covariates. More recently attention has focused on settings with a large number of covariates. In this paper we extend lessons from the earlier literature to this new setting. We propose that in addition to reporting point estimates and standard errors, researchers report results from a number of supplementary analyses to assist in assessing the credibility of their estimates.Citation
Athey, Susan, Guido Imbens, Thai Pham, and Stefan Wager. 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges." American Economic Review, 107 (5): 278-81. DOI: 10.1257/aer.p20171042Additional Materials
JEL Classification
- C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- C31 Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- C51 Model Construction and Estimation