论文标题
通过对抗平衡表示，随着时间的推移估算反事实治疗结果
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
论文作者
论文摘要
确定何时对患者进行治疗以及如何随着时间的推移在多种治疗中进行选择是一些现有解决方案的重要医疗问题。在本文中，我们介绍了反事实反复网络（CRN），这是一种新型的序列模型，它利用越来越多的患者观察数据随着时间的推移估算治疗效果并回答此类医疗问题。为了处理随着时变的混杂因素的偏见，影响了观察数据中治疗分配政策的协变量，CRN使用域的对抗训练来建立患者历史的平衡表示。在每个时间步长，CRN构建了一种治疗不变的表示，该表示可以消除患者病史和治疗分配之间的关联，因此可以可靠地用于做出反事实预测。在肿瘤生长的模拟模型（随时间依赖性的混杂程度变化）上，我们显示了与当前的最新方法相比，我们的模型如何在估计反事实和选择正确的治疗和治疗时间方面如何达到较低的误差。
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.
