In this paper, we propose a doubly robust method to estimate the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of interest for analyzing heterogeneity are of much lower dimension. Our proposed estimator is doubly robust and avoids the curse of dimensionality. We propose a uniform confidence band that is easy to compute, and we illustrate its usefulness via Monte Carlo experiments and an application to the effects of smoking on birth weights.
Authors
Research Fellow Columbia University
Sokbae is an IFS Research Fellow and a Professor at Columbia University, with an interest in Econometrics, Applied Microeconomics and Statistics.
SNU
Ryo Okui
Journal article details
- DOI
- 10.1002/jae.2574
- Publisher
- Wiley
- Issue
- Volume 32, Issue 7, May 2017
Suggested citation
S, Lee and R, Okui and Y, Whang. (2017). 'Doubly robust uniform confidence band for the conditional average treatment effect function' 32(7/2017)
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