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

• URL: http://arxiv.org/abs/2003.10551v1
• Date: Mon, 23 Mar 2020 21:08:51 GMT
• Title: G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
• Authors: Rui Li, Zach Shahn, Jun Li, Mingyu Lu, Prithwish Chakraborty, Daby Sow, Mohamed Ghalwash, Li-wei H. Lehman
• Abstract summary: G-computation is a method for estimating expected counterfactual outcomes under dynamic time-varying treatment strategies. This paper introduces G-Net, a novel sequential deep learning framework for G-computation. We evaluate alternative G-Net implementations using realistically complex temporal simulated data obtained from CVSim.
• Score: 11.361895456942374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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