G-net: a recurrent network approach to g-computation for counterfactual prediction under a dynamic treatment regime

Yuria Utsumi, Rui Li, Stephanie Hu, MingYu Lu, Prithwish Chakraborty, Daby Sow, Mohamed Ghalwash, Zach Shahn, Li-wei H. Lehman

Abstract: Counterfactual prediction is a fundamental task in decision-making. This paper introduces G-Net, a sequential deep learning framework for counterfactual prediction under dynamic time-varying treatment strategies in complex longitudinal settings. G-Net is based upon g-computation, a causal inference method for estimating effects of general dynamic treatment strategies. Past g-computation implementations have mostly been built using classical regression models. G-Net instead adopts a recurrent neural network framework to capture complex temporal and nonlinear dependencies in the data. To our knowledge, G-Net is the first g-computation based deep sequential modeling framework that provides estimates of treatment effects under\em {dynamic} and\em {time-varying} treatment strategies. We evaluate G-Net using simulated longitudinal data from two sources: CVSim, a mechanistic model of the cardiovascular system, and a pharmacokinetic simulation of tumor growth. G-Net outperforms both classical and state-of-the-art counterfactual prediction models in these settings.