Replication data for: Double/Debiased/Neyman Machine Learning of Treatment Effects
Principal Investigator(s): View help for Principal Investigator(s) Victor Chernozhukov; Denis Chetverikov; Mert Demirer; Esther Duflo; Christian Hansen; Whitney Newey
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
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| P2017_1038_data | 10/12/2019 11:29:AM | ||
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text/plain | 14.6 KB | 10/12/2019 07:29:AM |
Project Citation:
Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esther, Hansen, Christian, and Newey, Whitney. Replication data for: Double/Debiased/Neyman Machine Learning of Treatment Effects. Nashville, TN: American Economic Association [publisher], 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113505V1
Project Description
Summary:
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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.
Scope of Project
JEL Classification:
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C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
C31 Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
C31 Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
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