Identifiability and Exchangeability for Direct and Indirect Effects

TLDR

The study addresses separating direct effects of an exposure from indirect effects mediated by an intermediate variable. When exposure and intermediate do not interact and the intermediate can be blocked, a randomized trial of both exposure and the blocking intervention, analyzed with G‑computation, can disentangle direct from indirect effects. Adjustment for the intermediate is biased; direct and indirect effects are not identifiable when only exposure is randomized, and even with interaction they cannot be separated, but the preventable fraction of exposure‑induced disease can be estimated via G‑computation with additional confounder data. Epidemiology 1992; 3:143–155.

Abstract

We consider the problem of separating the direct effects of an exposure from effects relayed through an intermediate variable (indirect effects). We show that adjustment for the intermediate variable, which is the most common method of estimating direct effects, can be biased. We also show that, even in a randomized crossover trial of exposure, direct and indirect effects cannot be separated without special assumptions; in other words, direct and indirect effects are not separately identifiable when only exposure is randomized. If the exposure and intermediate never interact to cause disease and if intermediate effects can be controlled, that is, blocked by a suitable intervention, then a trial randomizing both exposure and the intervention can separate direct from indirect effects. Nonetheless, the estimation must be carried out using the G-computation algorithm. Conventional adjustment methods remain biased. When exposure and the intermediate interact to cause disease, direct and indirect effects will not be separable even in a trial in which both the exposure and the intervention blocking intermediate effects are randomly assigned. Nonetheless, in such a trial, one can still estimate the fraction of exposure-induced disease that could be prevented by control of the intermediate. Even in the absence of an intervention blocking the intermediate effect, the fraction of exposure-induced disease that could be prevented by control of the intermediate can be estimated with the G-computation algorithm if data are obtained on additional confounding variables. (Epidemiology 1992;3:143–155)