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Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders

Kara E. Rudolph et al.

Biostatistics2024https://doi.org/10.1093/biostatistics/kxae012article
ABDC A
Weight
0.67

Abstract

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.

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https://doi.org/https://doi.org/10.1093/biostatistics/kxae012

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@article{kara2024,
  title        = {{Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders}},
  author       = {Kara E. Rudolph et al.},
  journal      = {Biostatistics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1093/biostatistics/kxae012},
}

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Evidence weight

0.67

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.85 × 0.4 = 0.34
M · momentum0.72 × 0.15 = 0.11
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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