1. Double machine learning for sample selection models
| Title | Double machine learning for sample selection models |
|---|---|
| Author info | Michela Bia ... [et al.] |
| Author | Bia Michela (34%) |
| Co-authors | Huber Martin (33%) Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky |
| Source document | Journal of business & economic statistics. Vol. 42, no. 3 (2024), pp. 958-969. - Philadelphia : Taylor & Francis, 2024 |
| Keywords | strojové učenie - machine learning odhadovanie - estimating |
| Form. Descr. | články - journal articles |
| Language | English |
| Country | United States of America |
| Annotation | This article considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, 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 efficient score functions, which imply the robustness of treatment effect 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 overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data. The estimator is available in the causalweight package for the statistical software R. |
| URL | Link na plný text Link na plný text |
| Public work category | ADC |
| No. of Archival Copy | 54594 |
| Catal.org. | BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici |
| Database | xpca - PUBLIKAČNÁ ČINNOSŤ |
| References | PERIODIKÁ-Súborný záznam periodika |