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Causal mediation analysis with double machine learning
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 
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