Vytlačiť
1. Double machine learning for sample selection models
| Názov | Double machine learning for sample selection models |
|---|---|
| Aut.údaje | Michela Bia ... [et al.] |
| Autor | Bia Michela (34%) |
| Spoluautori | Huber Martin (33%) Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky |
| Zdroj.dok. | Journal of business & economic statistics. Vol. 42, no. 3 (2024), pp. 958-969. - Philadelphia : Taylor & Francis, 2024 |
| Kľúč.slová | strojové učenie - machine learning odhadovanie - estimating |
| Form.deskr. | články - journal articles |
| Jazyk dok. | angličtina |
| Krajina | Spojené štáty |
| Anotácia | 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 |
| Kategória publikačnej činnosti | ADC |
| Číslo archívnej kópie | 54594 |
| Katal.org. | BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici |
| Báza dát | xpca - PUBLIKAČNÁ ČINNOSŤ |
| Odkazy | PERIODIKÁ-Súborný záznam periodika |