The finite sample performance of estimators for mediation analysis under sequential conditional independence

Martin Huber, Giovanni Mellace and Michael Lechner

No 1415, Economics Working Paper Series from University of St. Gallen, School of Economics and Political Science

Abstract: Using a comprehensive simulation study based on empirical data, this paper investigates the finite sample properties of different classes of parametric and semi-parametric estimators of (natural or pure) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers and considers variations of several features of the data generating process and the implementation of the estimators that are of practical relevance. We find that no estimator performs uniformly best (in terms of root mean squared error) in all simulations. Overall, so-called ‘g-computation’ dominates. However, differences between estimators are often (but not always) minor in the various setups and the relative performance of the methods often (but not always) varies with the features of the data generating process.

Keywords: Causal mechanisms; direct effects; indirect effects; simulation; empirical Monte Carlo Study; causal channels; mediation analysis; causal pathways (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2014-06, Revised 2014-11
New Economics Papers: this item is included in nep-cmp and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Related works:
Journal Article: The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:usg:econwp:2014:15

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