Structural Nested Mean Models and G-Estimation: Part I
Structural nested mean models are an important method for causal inference in settings with time-varyings treatments.
They model the difference between two treatment regimes and naturally incorporate effect modification, making them a
useful option in studies investigating personalised approaches to medical treatment.
In this post, we'll introduce the basics of structural nested mean models, and the associated method of G-estimation
in a single decision setting.
