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Evaluating (weighted) dynamic treatment effects by double machine learning
Názov Evaluating (weighted) dynamic treatment effects by double machine learning Aut.údaje Hugo Bodory, Martin Huber, Lukáš Laffers Autor Bodory Hugo (34%)
Spoluautori Huber Martin (33%)
Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky
Zdroj.dok. The Econometrics Journal. Vol. 25, no. 3 (2022), pp. 628-648. - Londýn : Royal Economic Society, 2022 Kľúč.slová strojové učenie - machine learning intervencie Form.deskr. články - journal articles Jazyk dok. angličtina Krajina Veľká Británia Anotácia We consider evaluating the causal effects of dynamic treatments, i.e.. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption. To this end, we make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate (local) misspecifications of the dynamic outcome and treatment models. This robustness property permits approximating outcome and treatment models by double machine learning even under high-dimensional covariates. In addition to effect estimation for the total population, we consider weighted estimation that permits assessing dynamic treatment effects in specific subgroups. e.g.. among those treated in the first treatment period. We demonstrate that the estimators are asymptotically normal and root n-consistent under specific regularity conditions and investigate their finite sample properties in a simulation study. Finally, we apply the methods to the Job Corps study. URL Link na zdrojový dokument Kategória publikačnej činnosti ADC Číslo archívnej kópie 52191 Katal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Báza dát xpca - PUBLIKAČNÁ ČINNOSŤ Odkazy PERIODIKÁ-Súborný záznam periodika 
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