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Doubly Robust Uniform Confidence Band For The Conditional Average Treatment Effect Function

Author

Listed:
  • SOKBAE LEE

    () (Seoul National University, Institute for Fiscal Studies)

  • RYO OKUI

    () (Kyoto University, VU University Amsterdam)

  • YOON-JAE WHANG

    () (Seoul National University)

Abstract

In this paper, we propose a doubly robust method to present the het- erogeneity of the average treatment e ect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment e ect but the covariates of interest for analyzing heterogeneity are of much lower dimension. Our proposed estimator is doubly ro- bust and avoids the curse of dimensionality. We propose a uniform con dence band that is easy to compute, and we illustrate its usefulness via Monte Carlo experiments and an application to the e ects of smoking on birth weights.

Suggested Citation

  • Sokbae Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly Robust Uniform Confidence Band For The Conditional Average Treatment Effect Function," KIER Working Papers 931, Kyoto University, Institute of Economic Research.
  • Handle: RePEc:kyo:wpaper:931
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    References listed on IDEAS

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    8. Uysal, S. Derya, 2013. "Doubly Robust Estimation of Causal Effects with Multivalued Treatments," Economics Series 297, Institute for Advanced Studies.
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    Citations

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    Cited by:

    1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    2. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Mar 2019.
    3. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, Institute of Labor Economics (IZA).
    4. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised Sep 2019.
    5. Pedro H. C. Sant'Anna, 2016. "Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes," Papers 1612.02090, arXiv.org, revised Sep 2017.
    6. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    7. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2019. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201905, University of Kansas, Department of Economics, revised Mar 2019.
    8. Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2019. "Estimation of Conditional Average Treatment Effects with High-Dimensional Data," Papers 1908.02399, arXiv.org, revised Aug 2019.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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