Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators.

Area of Science:

• Causal Inference
• Epidemiology
• Statistical Modeling

Background:

• Traditional path analysis for multiple mediators requires strict assumptions like correct causal structure and no unobserved confounding.
• Violations of these assumptions can lead to inaccurate conclusions about mediator roles in treatment-outcome pathways.

Purpose of the Study:

• Introduce a novel definition of indirect effects: interventional indirect effects.
• Develop unbiased estimation methods for these effects, particularly in complex scenarios with interacting mediators.

Main Methods:

• Define interventional indirect effects based on causal inference principles.
• Demonstrate unbiased estimation under linear and additive mean models.
• Propose novel estimators for situations with mediator-outcome moderation and treatment-dependent mediator covariance.

Main Results:

• Interventional indirect effects can be estimated without stringent assumptions on mediator structure or confounding.
• Under specific models, estimators for interventional indirect effects share a form with product-of-coefficient estimators, offering unbiasedness with a new interpretation.
• Novel methods address indirect effects arising from mutual mediator dependence.

Conclusions:

• Interventional indirect effects provide a robust and interpretable alternative for analyzing multiple mediators.
• The proposed methods enhance causal inference accuracy when mediator relationships are complex or unmeasured confounding is a concern.