1. Causal mediation analysis with double machine learning
Title | Causal mediation analysis with double machine learning |
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Author info | Helmut Farbmacher ... [et al.] |
Author | Farbmacher Helmut (20%) |
Co-authors | Huber Martin 1980- (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 |