Equivalence between direct and indirect effects with different sets of intermediate variables and covariates
This paper deals with the concept of equivalence between direct and indirect effects of a treatment on a response using two sets of intermediate variables and covariates. First, we provide criteria for testing whether two sets of variables can estimate the same direct and indirect effects. Next, based on the proposed criteria, we discuss the variable selection problem from the viewpoint of estimation accuracy of direct and indirect effects, and show that selecting a set of variables that has a direct effect on a response cannot always improve estimation accuracy, which is contrary to the situation found in linear regression models. These results enable us to judge whether different sets of variables can yield the same direct and indirect effects and thus help us select appropriate variables to estimate direct and indirect effects with cost reduction or estimation accuracy.