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Replication data for: Double/Debiased/Neyman Machine Learning of Treatment Effects

Version
V0
Resource Type
Dataset
Creator
  • Chernozhukov, Victor
  • Chetverikov, Denis
  • Demirer, Mert
  • Duflo, Esther
  • Hansen, Christian
  • Newey, Whitney
Publication Date
2017-05-01
Description
  • Abstract

    Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.
Availability
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Relations
  • Is supplement to
    DOI: 10.1257/aer.p20171038 (Text)
Publications
  • Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey. American Economic Review, American Economic Review, 107, no. 5 (n.d.): 261–65. https://doi.org/10.1257/aer.p20171038.
    • ID: 10.1257/aer.p20171038 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-12

Chernozhukov, Victor; Chetverikov, Denis; Demirer, Mert; Duflo, Esther; Hansen, Christian et. al. (2017): Replication data for: Double/Debiased/Neyman Machine Learning of Treatment Effects. Version: V0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E113505