Paper Info
Title
G-Net: a Recurrent Network Approach to G-Computation for Counterfactual Prediction Under a Dynamic Treatment Regime.
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.
Year
Venue
DocType
2021
Annual Conference on Neural Information Processing Systems
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
11
Name
Order
Citations
PageRank
Rui Li11916.31
Stephanie Hu200.34
Mingyu Lu332.59
Yuria Utsumi411.03
Prithwish Chakraborty500.34
Daby M. Sow613117.69
Piyush Madan701.35
Jun Li89854.54
Mohamed Ghalwash900.68
Zach Shahn1000.34
Li-Wei H. Lehman1100.34