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Title The Econometrics Journal Issue data Londýn : Royal Economic Society , 2022 ISSN 1368-42211368-423X Form. Descr. časopisy - journals, elektronické časopisy - electronic journals Year, No. Vol. 25 no. 3 (2022) Language English Country Great Britian URL Link na zdrojový dokument Public work category GII Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ References - PERIODIKÁ - Súborný záznam periodika (1) - PUBLIKAČNÁ ČINNOSŤ References PERIODIKÁ-Súborný záznam periodika ARTICLES 2022: Evaluating dynamic treatment effects by double machine learning 
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Title The Econometrics Journal Issue data Londýn : Royal Economic Society , 2022 ISSN 1368-42211368-423X Form. Descr. časopisy - journals, elektronické časopisy - electronic journals Year, No. Vol. 25 no. 2 (2022) Language English Country Great Britian URL Link na zdrojový dokument Public work category GII Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ References - PERIODIKÁ - Súborný záznam periodika (1) - PUBLIKAČNÁ ČINNOSŤ References PERIODIKÁ-Súborný záznam periodika ARTICLES 2022: Causal mediation analysis with double machine learning journal
Title Causal mediation analysis with double machine learning Author info Helmut Farbmacher ... [et al.] Author Farbmacher Helmut (20%)
Co-authors Huber Martin (20%)
Lafférs Lukáš 1986- (20%) UMBFP10 - Katedra matematiky
Langen Henrika (20%)
Spindler Martin (20%)
Source document The Econometrics Journal. Vol. 25, no. 2 (2022), pp. 277-300. - Londýn : Royal Economic Society, 2022 Keywords matematické metódy - mathematical methods ekonomika - economics strojové učenie - machine learning analýza kauzálneho sprostredkovania - causal mediation analysis Form. Descr. články - journal articles Language English Country Great Britian Annotation This paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional setting. The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, which are robust with respect to misspecifications of the outcome, mediator, and treatment models. This property is key for selecting these models by double machine learning, which is combined with data splitting to prevent overfitting. We demonstrate that the effect estimators are asymptotically normal and n−1/2-consistent under specific regularity conditions and investigate the finite sample properties of the suggested methods in a simulation study when considering lasso as machine learner. We also provide an empirical application to the US National Longitudinal Survey of Youth, assessing the indirect effect of health insurance coverage on general health operating via routine checkups as mediator, as well as the direct effect. URL Link na plný text Public work category ADC No. of Archival Copy 51676 Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ References PERIODIKÁ-Súborný záznam periodika 
article
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 article
Title The Econometrics Journal Issue data Londýn : Royal Economic Society , 1998- ISSN 1368-42211368-423X Form. Descr. časopisy - journals, elektronické časopisy - electronic journals Language English Country Great Britian URL Link na zdrojový dokument Public work category GII Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xszp - PERIODIKÁ - Súborný záznam periodika References (4) - PUBLIKAČNÁ ČINNOSŤ journal