G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes

By Zachary Shahn, Jun Li, Mingyu Lu, Prithwish Chakraborty, Daby M. Sow, Mohamed F. Ghalwash, Li-wei H. Lehman. 2020

22 citations recorded by the source corpus.

Counterfactual prediction is a fundamental task in decision-making. G-computation is a method for estimating expected counterfactual outcomes under dynamic time-varying treatment strategies. Existing G-computation implementations have mostly employed classical regression models with limited capacity to capture complex temporal and nonlinear dependence structures. This paper introduces G-Net, a novel sequential deep learning framework for G-computation that can handle complex time series data while imposing minimal modeling assumptions and provide estimates of individual or population-level time varying treatment effects. We evaluate alternative G-Net implementations using realistically complex temporal simulated data obtained from CVSim, a mechanistic model of the cardiovascular system.

Subfields: Artificial Intelligence, Statistics and Probability, Causal Inference & Discovery, Medical & Health AI

Citations in the corpus

• Causal machine learning for predicting treatment outcomes (2024, unknown): “To address this issue, ML needs to be embedded in a causal framework.”