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

arXiv: https://arxiv.org/abs/1503.01589 · HTML: https://mlsanity.com/paper/1503.01589/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2015.

## Built on by (most cited first, 15 of 15)

- [Estimating Average Treatment Effects via Orthogonal Regularization](https://mlsanity.com/paper/2101.08490/) (2021)
- [Robust and Agnostic Learning of Conditional Distributional Treatment Effects](https://mlsanity.com/paper/2205.11486/) (2022)
- [Estimating the Long-Term Effects of Novel Treatments](https://mlsanity.com/paper/2103.08390/) (2021)
- [Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions](https://mlsanity.com/paper/2203.02605/) (2022)
- [Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves](https://mlsanity.com/paper/2111.03950/) (2021)
- [Doubly Robust Counterfactual Classification](https://mlsanity.com/paper/2301.06199/) (2023)
- [Double Machine Learning for Static Panel Models with Fixed Effects](https://mlsanity.com/paper/2312.08174/) (2023)
- [A primer on optimal transport for causal inference with observational data](https://mlsanity.com/paper/2503.07811/) (2025)
- [DeepBlip: Estimating Conditional Average Treatment Effects Over Time](https://mlsanity.com/paper/2511.14545/) (2025)
- [Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data](https://mlsanity.com/paper/2310.10559/) (2023)
- [Counterfactual Prediction Under Selective Confounding](https://mlsanity.com/paper/2310.14064/) (2023)
- [Dynamic covariate balancing: estimating treatment effects over time with potential local projections](https://mlsanity.com/paper/2103.01280/) (2021)
- [Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data](https://mlsanity.com/paper/2606.01539/) (2026)
- [Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity](https://mlsanity.com/paper/2607.10540/) (2026)
- [When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments](https://mlsanity.com/paper/2609.17772/) (2026)

## Abstract (arXiv)

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.