WITHDRAWN Efficient Uncertainty Quantification for Multi-Level Causal Mediation Analysis

Music City Center 

Causal mediation analysis is a popular tool for studying complicated causal dependence between multiple variables. We investigate the extent to which the effect of an exposure, X, on a response, Y, is mediated by a third variable, M. One common approach involves fitting regression models and identifying mediation effects with functions of the regression parameters. Unfortunately, uncertainty quantification for these mediation effects is often non-trivial in even simple settings. Existing methods in the literature tend to rely on intensive computation, and are thus slow.
We propose an analytical method for obtaining standard errors of estimated mediation effects using the δ-method. We compare the performance of our method with its main competitor using Monte Carlo studies and analysis of a dataset on adherence to pandemic lockdown measures across 11 countries.

Causal mediation analysis

Mixed-effects models

Multi-level models 

Section on Statistics in Epidemiology