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Double machine learning for sample selection models
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$a 10.1080/07350015.2023.2271071 $2 DOI 035 $a biblio/1190759 $2 CREPC2 100 $a 20240708d2024 m y slo 03 ba 101 0-
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$a Double machine learning for sample selection models $f Michela Bia ... [et al.] 330 $a 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. 463 -1
$1 001 umb_un_cat*0330056 $1 011 $a 0735-0015 $1 011 $a 1537-2707 $1 200 1 $a Journal of business & economic statistics $v Vol. 42, no. 3 (2024), pp. 958-969 $1 210 $a Philadelphia $c Taylor & Francis $d 2024 606 0-
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$3 umb_un_auth*0052417 $a odhadovanie $X estimating 608 $3 umb_un_auth*0273282 $a články $X journal articles 700 -1
$3 umb_un_auth*0306068 $a Bia $b Michela $4 070 $9 34 701 -1
$3 umb_un_auth*0249133 $a Huber $b Martin $4 070 $9 33 701 -1
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