Structural Nested Models and G-estimation: The Partially Realized Promise

TL;DR

This paper reviews the development, advantages, and limitations of structural nested models and G-estimation for causal inference, highlighting their potential and challenges for broader application in research.

Contribution

It provides a comprehensive overview of SNMs and G-estimation, analyzing their advantages, reasons for limited adoption, and potential extensions.

Findings

SNMs and G-estimation offer advantages over other methods.

Limited application in practice due to various challenges.

Extensions of models and methods are discussed.

Abstract

Structural nested models (SNMs) and the associated method of G-estimation were first proposed by James Robins over two decades ago as approaches to modeling and estimating the joint effects of a sequence of treatments or exposures. The models and estimation methods have since been extended to dealing with a broader series of problems, and have considerable advantages over the other methods developed for estimating such joint effects. Despite these advantages, the application of these methods in applied research has been relatively infrequent; we view this as unfortunate. To remedy this, we provide an overview of the models and estimation methods as developed, primarily by Robins, over the years. We provide insight into their advantages over other methods, and consider some possible reasons for failure of the methods to be more broadly adopted, as well as possible remedies. Finally, we consider several extensions of the standard models and estimation methods.