Doubly robust uniform confidence band for the conditional average treatment effect function

Sokbae Lee, Ryo Okui, Yoon-Jae Wang

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.

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Year of publication:	January 2016 ; This version: January 2016
Authors:	Lee, Sokbae ; Okui, Ryo ; Wang, Yoon-Jae
Publisher:	London : Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL
Subject:	average treatment effect conditional on covariates \| uniform confidence band \| double robustness \| Gaussian approximation \| Schätztheorie \| Estimation theory \| Nichtparametrisches Verfahren \| Nonparametric statistics \| Kausalanalyse \| Causality analysis \| Robustes Verfahren \| Robust statistics \| Bootstrap-Verfahren \| Bootstrap approach