Abstract
Instrumental variables is a popular method in epidemiology and related fields, to estimate causal effects in the presence of unmeasured confounding. Traditionally, instrumental variable analyses have been confined to linear models, in which the causal parameter of interest is typically estimated with two-stage least squares. Recently, the methodology has been extended in several directions, including two-stage estimation and so-called G-estimation in nonlinear (e. g. logistic and Cox proportional hazards) models. This paper presents a new R package, ivtools, which implements many of these new instrumental variable methods. We briefly review the theory of two-stage estimation and G-estimation, and illustrate the functionality of the ivtools package by analyzing publicly available data from a cohort study on vitamin D and mortality.
On two-stage estimation of structural instrumental variable models
