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Evaluating (weighted) dynamic treatment effects by double machine learning
Title Evaluating (weighted) dynamic treatment effects by double machine learning Author info Hugo Bodory, Martin Huber, Lukáš Laffers Author Bodory Hugo (34%)
Co-authors Huber Martin (33%)
Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky
Source document The Econometrics Journal. Vol. 25, no. 3 (2022), pp. 628-648. - Londýn : Royal Economic Society, 2022 Keywords strojové učenie - machine learning intervencie Form. Descr. články - journal articles Language English Country Great Britian Annotation 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 Public work category ADC No. of Archival Copy 52191 Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ References PERIODIKÁ-Súborný záznam periodika 
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