Self-separated and self-connected models for mediator and outcome missingness in mediation analysis

Music City Center 

Missing data is a common problem that challenges the study of treatment effects. In the context of mediation analysis, this paper addresses the missingness in the following two key variables, mediator and outcome, focusing on identification. We first consider self-separated missingness models, where identification is achieved by conditional independence assumptions only. This class of models is somewhat limited because it is constrained by the need to remove a certain number of connections from the model. We then turn to self-connected missingness models, where identification relies on information from so-called shadow variables. This class of models
turns out to contain substantial variation, allowing models with built-in shadow variables (mediator, outcome or covariates) and models with auxiliary shadow variables at different positions in the causal structure (relative to the mediator and outcome). In order to improve the practical plausibility of the missingness mechanisms, when constructing the models, we allow for dependencies due to unobserved causes of the missingness wherever possible. In this exploration, we develop theory where needed. This results in templates for identification in the mediation setting, generally useful identification techniques, and perhaps most importantly a synthesis and substantial extension of shadow variable theory. This is joint work with Razieh Nabi, Fan Yang and Elizabeth Stuart.

MNAR

mediation analysis