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

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  • Martin Huber
  • Michael Lechner
  • Giovanni Mellace

Abstract

Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric estimators 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.

Suggested Citation

  • Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    • Handle: RePEc:taf:jnlbes:v:34:y:2016:i:1:p:139-160
    DOI: 10.1080/07350015.2015.1017644
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    References listed on IDEAS

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    1. Stefanie Behncke & Markus Frölich & Michael Lechner, 2010. "A Caseworker Like Me - Does The Similarity Between The Unemployed and Their Caseworkers Increase Job Placements?," Economic Journal, Royal Economic Society, vol. 120(549), pages 1430-1459, December.
    2. Arun Advani & Tymon Sloczynski, 2013. "Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies," CeMMAP working papers CWP64/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    4. Andrew Gelman & Guido Imbens, 2013. "Why ask Why? Forward Causal Inference and Reverse Causal Questions," NBER Working Papers 19614, National Bureau of Economic Research, Inc.
    5. Flores, Carlos A. & Flores-Lagunes, Alfonso, 2009. "Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness," IZA Discussion Papers 4237, Institute for the Study of Labor (IZA).
    6. Newey, Whitney K., 1984. "A method of moments interpretation of sequential estimators," Economics Letters, Elsevier, vol. 14(2-3), pages 201-206.
    7. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
    8. Raymond Hicks & Dustin Tingley, 2011. "Causal mediation analysis," Stata Journal, StataCorp LP, vol. 11(4), pages 605-619, December.
    9. repec:tpr:restat:v:99:y:2017:i:1:p:180-183 is not listed on IDEAS
    10. Imai, Kosuke & Yamamoto, Teppei, 2013. "Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments," Political Analysis, Cambridge University Press, vol. 21(02), pages 141-171, March.
    11. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    12. Martin Huber & Michael Lechner & Giovanni Mellace, 2017. "Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
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    Cited by:

    1. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    2. Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2016. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," IZA Discussion Papers 9706, Institute for the Study of Labor (IZA).
    3. Stephen Whelan, 2017. "Does homeownership affect education outcomes?," IZA World of Labor, Institute for the Study of Labor (IZA), pages 342-342, April.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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