Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of the exposure on the outcome can be decomposed into an indirect effect, i.e. the effect explained by the mediators jointly, and a direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are possible in presence of independent or sequential mediators.

Applied causal inference methods for sequential mediators

Zugna, D;Popovic, M;Fasanelli, F;Scelo, G;Richiardi, L
2022-01-01

Abstract

Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of the exposure on the outcome can be decomposed into an indirect effect, i.e. the effect explained by the mediators jointly, and a direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are possible in presence of independent or sequential mediators.
2022
22
1
301
313
Causal inference; Direct and indirect effects; Imputation; Mediation analysis; Sequential mediators; Weighting
Zugna, D; Popovic, M; Fasanelli, F; Heude, B; Scelo, G; Richiardi, L
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1880585
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