Causal mediation analysis with double machine learning

Helmut Farbmacher, Martin Huber, Henrika Langen () and Martin Spindler ()
Additional contact information
Henrika Langen: Faculty of Economics and Social Sciences, Postal: Bd. de Pérolles 90, 1700 Fribourg, Switzerland
Martin Spindler: Universität Hamburg, Postal: 20148 Hamburg, https://www.bwl.uni-hamburg.de/matstat/team/

No 515, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland

Abstract: This paper combines causal mediation analysis with double machine learning to control for observed confounders in a data-driven way under a selection-on-observables assumption in a high-dimensional setting. We consider the average indirect effect of a binary treatment operating through an intermediate variable (or mediator) on the causal path between the treatment and the outcome, as well as the unmediated direct effect. Estimation is based on efficient score functions, which possess a multiple robustness property w.r.t. 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 in the estimation of the effects of interest. We demonstrate that the direct and indirect effect estimators are asymptotically normal and root-n 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 U.S. 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. We find a moderate short term effect of health insurance coverage on general health which is, however, not mediated by routine checkups.

Keywords: Mediation; direct and indirect effects; causal mechanisms; double machine learning; effcient score (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2020-05-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-hea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Causal mediation analysis with double machine learning (2022)
Working Paper: Causal mediation analysis with double machine learning (2021)
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