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Direct Effect Models

Author

Listed:
  • van der Laan Mark J.

    (University of California, Berkeley)

  • Petersen Maya L

    (University of California, Berkeley)

Abstract

The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is not mediated by an intermediate variable (the direct effect of the treatment) is often relevant to mechanistic understanding and to the design of clinical and public health interventions. Robins, Greenland and Pearl develop counterfactual definitions for two types of direct effects, natural and controlled, and discuss assumptions, beyond those of sequential randomization, required for the identifiability of natural direct effects. Building on their earlier work and that of others, this article provides an alternative counterfactual definition of a natural direct effect, the identifiability of which is based only on the assumption of sequential randomization. In addition, a novel approach to direct effect estimation is presented, based on assuming a model directly on the natural direct effect, possibly conditional on a subset of the baseline covariates. Inverse probability of censoring weighted estimators, double robust inverse probability of censoring weighted estimators, likelihood-based estimators, and targeted maximum likelihood-based estimators are proposed for the unknown parameters of this novel causal model.

Suggested Citation

  • van der Laan Mark J. & Petersen Maya L, 2008. "Direct Effect Models," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-27, October.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:23
    DOI: 10.2202/1557-4679.1064
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    References listed on IDEAS

    as
    1. Sara Geneletti, 2007. "Identifying direct and indirect effects in a non‐counterfactual framework," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 199-215, April.
    2. Mark van der Laan & Maya Petersen, 2004. "Estimation of Direct and Indirect Causal Effects in Longitudinal Studies," U.C. Berkeley Division of Biostatistics Working Paper Series 1155, Berkeley Electronic Press.
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    Citations

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    Cited by:

    1. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    2. Zheng Wenjing & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation of Natural Direct Effects," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-40, January.
    3. Kara E. Rudolph & Iván Díaz, 2022. "When the ends do not justify the means: Learning who is predicted to have harmful indirect effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 573-589, December.
    4. Shpitser Ilya & VanderWeele Tyler J, 2011. "A Complete Graphical Criterion for the Adjustment Formula in Mediation Analysis," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-24, March.
    5. Shuxi Zeng & Elizabeth C. Lange & Elizabeth A. Archie & Fernando A. Campos & Susan C. Alberts & Fan Li, 2023. "A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 197-218, June.
    6. Numair Sani & Yizhen Xu & AmirEmad Ghassami & Ilya Shpitser, 2021. "Multiply Robust Causal Mediation Analysis with Continuous Treatments," Papers 2105.09254, arXiv.org, revised Feb 2024.
    7. Stijn Vansteelandt & Tyler J. VanderWeele, 2012. "Natural Direct and Indirect Effects on the Exposed: Effect Decomposition under Weaker Assumptions," Biometrics, The International Biometric Society, vol. 68(4), pages 1019-1027, December.
    8. Guanglei Hong & Jonah Deutsch & Heather D. Hill, 2013. "Ratio-of-Mediator-Probability Weighting for Causal Mediation Analysis in the Presence of Treatment-by-Mediator Interaction," Working Papers 2013-009, Human Capital and Economic Opportunity Working Group.

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