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

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  • Huber, Martin
  • Mellace, Giovanni
  • Lechner, Michael

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.

Suggested Citation

  • Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "The finite sample performance of estimators for mediation analysis under sequential conditional independence," Economics Working Paper Series 1415, University of St. Gallen, School of Economics and Political Science, revised Nov 2014.
  • Handle: RePEc:usg:econwp:2014:15
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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. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    3. 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.
    4. 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.
    5. 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.
    6. 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 of Labor Economics (IZA).
    7. Newey, Whitney K., 1984. "A method of moments interpretation of sequential estimators," Economics Letters, Elsevier, vol. 14(2-3), pages 201-206.
    8. 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.
    9. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
    10. Raymond Hicks & Dustin Tingley, 2011. "Causal mediation analysis," Stata Journal, StataCorp LP, vol. 11(4), pages 605-619, December.
    11. 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.
    12. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    13. 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(2), pages 141-171, April.
    14. 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.
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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. Advani, Arun & Sloczynski, Tymon, 2013. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," IZA Discussion Papers 7874, Institute of Labor Economics (IZA).
    3. Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019. "Mostly harmless simulations? Using Monte Carlo studies for estimator selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
    4. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    5. Lombardi, Stefano & van den Berg, Gerard J. & Vikström, Johan, 2020. "Empirical Monte Carlo evidence on estimation of Timing-of-Events models," Working Paper Series 2020:26, IFAU - Institute for Evaluation of Labour Market and Education Policy, revised 05 Jan 2021.
    6. Mellace, Giovanni & Pasquini, Alessandra, 2019. "Identify More, Observe Less: Mediation Analysis: Mediation Analysis Synthetic Control," Discussion Papers of Business and Economics 12/2019, University of Southern Denmark, Department of Business and Economics.
    7. Stephen Whelan, 2017. "Does homeownership affect education outcomes?," IZA World of Labor, Institute of Labor Economics (IZA), pages 342-342, April.
    8. Bijwaard, Govert & Alessie, Rob & Angelini, Viola, 2018. "The Effect of Early Life Health on Later Life Home Care Use: The Mediating Role of Household Composition," IZA Discussion Papers 11729, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Causal mechanisms; direct effects; indirect effects; simulation; empirical Monte Carlo Study; causal channels; mediation analysis; causal pathways;
    All these keywords.

    JEL classification:

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

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