Estimation of direct causal effects.

Maya L. Petersen, University of California, Berkeley
Sandra E. Sinisi, Univeristy of California, Berkeley
Mark J. van der Laan, University of Califoria, Berkeley

Abstract

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, when the exposure and intermediate interact to cause disease, 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 (controlled 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 (natural direct effect). Relying on examples, we illustrate the difference between controlled and natural direct effects. We present an estimation approach for natural direct effects that can be implemented using standard statistical software and review the assumptions underlying our approach, which are less restrictive than those proposed by previous authors.