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Multilevel mediation analysis with structured unmeasured mediator-outcome confounding

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  • Zhao, Yi
  • Luo, Xi

Abstract

Mediation analysis usually requires the assumption that there is no unmeasured mediator-outcome confounder. However, this may not hold in many social and scientific studies. Though various parametric and nonparametric mediation methods have been developed, this assumption remains instrumental, without which the causal effects are not identifiable unless alternative assumptions are imposed. To circumvent this, a multilevel parametric structural equation modeling framework is proposed to relax this no unmeasured mediator-outcome confounding assumption under a specific data setting inspired by a real experiment. Using the proposed framework, it is shown that the causal effects are identifiable and consistently estimated. Likelihood-based approaches are proposed with efficient optimization algorithms to estimate the parameters, including the unmeasured confounding effect, instead of performing sensitivity analysis. The asymptotic consistency is established. Using extensive simulations and a functional magnetic resonance imaging dataset, the improvement of the approaches over existing methods is demonstrated. The R package macc for implementation is available on CRAN.

Suggested Citation

  • Zhao, Yi & Luo, Xi, 2023. "Multilevel mediation analysis with structured unmeasured mediator-outcome confounding," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    • Handle: RePEc:eee:csdana:v:179:y:2023:i:c:s0167947322002031
    DOI: 10.1016/j.csda.2022.107623
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    References listed on IDEAS

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    1. Xi Luo & Dylan S. Small & Chiang-Shan R. Li & Paul R. Rosenbaum, 2012. "Inference With Interference Between Units in an fMRI Experiment of Motor Inhibition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 530-541, June.
    2. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    3. 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.
    4. Michael E. Sobel & Martin A. Lindquist, 2014. "Causal Inference for fMRI Time Series Data With Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 967-976, September.
    5. Hausman, Jerry A., 1983. "Specification and estimation of simultaneous equation models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 7, pages 391-448, Elsevier.
    6. Thomas R. Ten Have & Marshall M. Joffe & Kevin G. Lynch & Gregory K. Brown & Stephen A. Maisto & Aaron T. Beck, 2007. "Causal Mediation Analyses with Rank Preserving Models," Biometrics, The International Biometric Society, vol. 63(3), pages 926-934, September.
    7. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    8. Martin A. Lindquist, 2012. "Functional Causal Mediation Analysis With an Application to Brain Connectivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1297-1309, December.
    9. Isabel R. Fulcher & Ilya Shpitser & Stella Marealle & Eric J. Tchetgen Tchetgen, 2020. "Robust inference on population indirect causal effects: the generalized front door criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 199-214, February.
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