Identifying when to give treatments to patients and how to select among
multiple treatments over time are important medical problems with a few
existing solutions. In this paper, we introduce the Counterfactual Recurrent
Network (CRN), a novel sequence-to-sequence model that leverages the
increasingly available patient observational data to estimate treatment effects
over time and answer such medical questions. To handle the bias from
time-varying confounders, covariates affecting the treatment assignment policy
in the observational data, CRN uses domain adversarial training to build
balancing representations of the patient history. At each timestep, CRN
constructs a treatment invariant representation which removes the association
between patient history and treatment assignments and thus can be reliably used
for making counterfactual predictions. On a simulated model of tumour growth,
with varying degree of time-dependent confounding, we show how our model
achieves lower error in estimating counterfactuals and in choosing the correct
treatment and timing of treatment than current state-of-the-art methods.

介绍了一种新的序列到序列模型 —— 对立逆向网络，该模型利用患者日常观察数据，通过对领域敌对训练来处理时间变化混杂物的偏见，以可靠地作出反事实预测。在肿瘤生长的模拟模型中，比目前最先进的方法在估计反事实和选择正确的治疗和治疗时间方面实现了更低的误差。