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cwp031616.pdf
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In this paper, we propose a doubly robust method to present 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
![Sokbae "Simon" Lee](/sites/default/files/styles/square_desktop/public/2022-07/Simon%20Lee.jpg?itok=J8PnQSbc)
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.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
SNU
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Ryo Okui
Working Paper details
- DOI
- 10.1920/wp.cem.2016.0316
- Publisher
- Institute for Fiscal Studies
Suggested citation
S, Lee and R, Okui and Y, Whang. (2016). Doubly robust uniform confidence band for the conditional average treatment effect function. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/doubly-robust-uniform-confidence-band-conditional-average-treatment-effect-function (accessed: 30 June 2024).
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