Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects arises frequently in research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings. Although multivariable regression is commonly used to estimate direct effects, this approach requires assumptions beyond those required for the estimation of total causal effects. In addition, multivariable regression estimates a particular type of direct effect, the effect of an exposure on outcome fixing the intermediate at a specified level. Using the counterfactual framework, we distinguish this definition of a direct effect (Type 1 direct effect) from an alternative definition, in which the effect of the exposure on the intermediate is blocked, but the intermediate is otherwise allowed to vary as it would in the absence of exposure (Type 2 direct effect). When the intermediate and exposure interact to affect the outcome these two types of direct effects address distinct research questions. Relying on examples, we illustrate the difference between Type 1 and Type 2 direct effects. We propose an estimation approach for Type 2 direct effects that can be implemented using standard statistical software and illustrate its implementation using a numerical example. We also review the assumptions underlying our approach, which are less restrictive than those proposed by previous authors.
Petersen, Maya L. and van der Laan, Mark J., "Estimation of Direct Causal Effects" (September 2005). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 190.