Effect Decomposition in the Presence of an Exposure-Induced Mediator-Outcome Confounder
Year: 2014
Type: article
Abstract: Methods from causal mediation analysis have generalized the traditional approach to direct and indirect effects in the epidemiologic and social science literature by allowing for interaction and nonlinearities. However, the methods from the causal inference literature have themselves been subject to a major limitation, in that the so-called natural direct and indirect effects that are used are not identified from data whenever there is a mediator-outcome confounder that is also affected by the exposure. In this article, we describe three alternative approaches to effect decomposition that give quantities that can be interpreted as direct and indirect effects and that can be identified from data even in the presence of an exposure-induced mediator-outcome confounder. We describe a simple weighting-based estimation method for each of these three approaches, illustrated with data from perinatal epidemiology. The methods described here can shed insight into pathways and questions of mediation even when an exposure-induced mediator-outcome confounder is present.
Source: Epidemiology
Institutions: Harvard University, Ghent University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Dealing with effect decomposition and intermediate confounding in causal mediation analysis, Analytic Methods for HIV Treatment and Co-Factor Effects, Theory and methods for sufficient cause interactions, Theory and methods for interaction, Statistical Informatics for Cancer Research, G9000078055, Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
OpenAlex ID: W2084701241