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doublemachinelearningforsampleselectionmodels
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Double machine learning for sample selection models

BP2-STS

  • 2021
sample selection
double machine learning
doubly robust estimation
efficient score
English This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identifi cation, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and ecient score functions, which imply the robustness of treatment e ect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross- fitting) to prevent over fitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent under speci c regularity conditions concerning the machine learners and investigate their fi nite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data for evaluating the e ect of training on hourly wages which are only observed conditional on employment. The estimator is available in the causalweight package for the statistical software R.
Faculty
Faculté des sciences économiques et sociales et du management
Department
Département d'économie politique
Language
  • English
Classification
Economics
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Published version

License
License undefined
Open access status
green
Persistent URL
https://folia.unifr.ch/unifr/documents/322663
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