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A framework for Bayesian nonparametric inference for causal effects of mediation.

Chanmin Kim1, Michael J Daniels2, Bess H Marcus3

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, U.S.A.

Biometrics
|August 2, 2016
PubMed
Summary

This study introduces a Bayesian non-parametric framework to estimate causal mediation effects. The method uses flexible modeling and sensitivity analyses to assess direct and indirect effects in trials.

Keywords:
Causal inferenceDirichlet process mixtureSensitivity Analysis

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Area of Science:

  • Causal inference
  • Statistical modeling
  • Biostatistics

Background:

  • Estimating causal mediation effects is crucial for understanding intervention mechanisms.
  • Existing methods often rely on strong, untestable assumptions.
  • There is a need for flexible frameworks that can quantify uncertainty in causal effect estimation.

Purpose of the Study:

  • To propose a novel Bayesian non-parametric (BNP) framework for estimating causal mediation effects, including natural direct and indirect effects.
  • To develop a two-part strategy combining flexible modeling of observed data with sensitivity analyses for untestable assumptions.
  • To assess mediation in a physical activity promotion trial using the proposed framework.

Main Methods:

  • A two-part strategy: Part 1 uses BNP for flexible modeling of the joint distribution of outcome, mediator, and covariates.
  • Part 2 incorporates sensitivity parameters and priors to address untestable assumptions, enabling identification and estimation of causal parameters.
  • A Dirichlet process mixture of multivariate normals is specified as a prior for the joint distribution, yielding closed-form marginal distributions.

Main Results:

  • The framework allows for the estimation of causal direct and indirect effects.
  • Sensitivity analyses are proposed for both standard sequential ignorability and weakened conditional independence assumptions.
  • The approach was successfully applied to assess mediation in a physical activity promotion trial.

Conclusions:

  • The proposed Bayesian non-parametric framework offers a flexible and robust approach to estimating causal mediation effects.
  • The inclusion of sensitivity analyses enhances the reliability of causal effect estimates by quantifying uncertainty due to untestable assumptions.
  • This methodology provides a valuable tool for researchers investigating mediation in various experimental settings.