Causal mediation analysis with double machine learning

Mediation analysis via potential outcomes models

Helmut Farbmacher, Martin Huber, Lukáš Lafférs, Henrika Langen and Martin Spindler

The Econometrics Journal, 2022, vol. 25, issue 2, 277-300

Abstract: SummaryThis 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 -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.

Keywords: mediation; direct and indirect effects; causal mechanisms; double machine; learning; efficient score (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Working Paper: Causal mediation analysis with double machine learning (2021)
Working Paper: Causal mediation analysis with double machine learning (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:25:y:2022:i:2:p:277-300.

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