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The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence

    2016
Published in:
  • Journal of Business & Economic Statistics. - American Statistical Association. - 2016, vol. 34, no. 1, p. 139-160
Causal channels
Causal mechanisms
Direct effects
Empirical Monte Carlo study
Indirect effects
Simulation
English Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric stimators of (natural) 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 (DGP) 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 DGP.
Faculty
Faculté des sciences économiques et sociales et du management
Department
Département d'économie politique
Language
  • English
Classification
Economics
License
License undefined
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Persistent URL
https://folia.unifr.ch/unifr/documents/308278
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