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Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

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  • Susan Athey
  • Guido Imbens
  • Thai Pham
  • Stefan Wager

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.

Suggested Citation

  • Susan Athey & Guido Imbens & Thai Pham & Stefan Wager, 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges," American Economic Review, American Economic Association, vol. 107(5), pages 278-281, May.
    • Handle: RePEc:aea:aecrev:v:107:y:2017:i:5:p:278-81
    Note: DOI: 10.1257/aer.p20171042
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    References listed on IDEAS

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    Cited by:

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    10. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    11. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    12. Costanza Naguib, 2023. "Is the Impact of Opening the Borders Heterogeneous?," Diskussionsschriften dp2312, Universitaet Bern, Departement Volkswirtschaft.
    13. Christian Stetter & Philipp Mennig & Johannes Sauer, 2022. "Using Machine Learning to Identify Heterogeneous Impacts of Agri-Environment Schemes in the EU: A Case Study [The impact of agri-environmental schemes on farm performance in five EU member States: ," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 723-759.
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    21. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2018. "Deep Neural Networks for Estimation and Inference," Papers 1809.09953, arXiv.org, revised Sep 2019.
    22. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    23. Minkyung Kim & K. Sudhir & Kosuke Uetake, 2022. "A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design," Management Science, INFORMS, vol. 68(6), pages 4602-4630, June.
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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