46: Bias-corrected estimation in causal mediation analysis

Thomas M. Menino Convention & Exhibition Center 

Causal mediation analysis, based on the counterfactual approach, decomposes the total effect of an exposure into natural direct and indirect effects, with the mediation proportion (MP) quantifying the relative contribution of the mediator. However, the MP, along with its components, the natural indirect effect (NIE) and the natural direct effect (NDE), are functional estimators. In finite samples, their ratio and exponential forms can make them unstable and biased. This paper introduces the problem of transformation-induced bias in regression-based causal mediation analysis and proposes two likelihood-based bias-correction methods. These methods target exponential and ratio functionals, including the NDE, NIE, and MP, and provide closed-form corrections for continuous and binary outcomes and mediators, with or without exposure–mediator interaction. Simulation studies show that ordinary estimators exhibit notable finite-sample bias, especially for the MP and log-odds scale effects. In contrast, the proposed methods reduce relative bias and MSE while preserving large-sample properties. Real-data applications confirm more stable and precise inference, particularly for the MP.

causal mediation analysis

likelihood thoery

mediation proportion

transformation-induced bias

bias-correction 

Section on Statistics in Epidemiology