Controlled direct and mediated effects: definition, identification and bounds

Abstract

Results are given which provide bounds for controlled direct effects when the no-unmeasured-confounding assumptions required for the identification of these effects do not hold. Previous results concerning bounds for controlled direct effects rely on monotonicity relationships between the treatment, mediator and the outcome themselves; the results presented in this paper instead assume that monotonicity relationships hold between the unmeasured confounding variable or variables and the treatment, mediator and outcome. Whereas prior results give bounds that contain the null hypothesis of no direct effect, the results presented here will in many instances yield bounds that do not contain the null hypothesis of no direct effect. For contexts in which a set of variables intercepts all paths between a treatment and an outcome, it is possible to provide a definition for a controlled mediated effect. We discuss the identification of these controlled mediated effects; the bounds for controlled direct effects are applicable also to controlled mediated effects. An example is given to illustrate how the results in the paper can be used to draw inferences about direct and mediated effects in the presence of unmeasured confounding variables.

Keywords: Bounds; causal inference; direct and indirect effects; mediation; unmeasured confounding.

Figure 1

Example illustrating direct effect of A on Y controlling for Z.

Figure 2

Causal directed acyclic graph to which Theorem 1 would apply.

Figure 3

Causal directed acyclic graph to which Theorem 2 would apply.