The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are no hidden confounders, an assumption that is not testable in practice and, if it does not hold, leads to biased estimates. In this paper, we develop the Time Series Deconfounder, a method that leverages the assignment of multiple treatments over time to enable the estimation of treatment effects in the presence of multi-cause hidden confounders. The Time Series Deconfounder uses a novel recurrent neural network architecture with multitask output to build a factor model over time and infer latent variables that render the assigned treatments conditionally independent; then, it performs causal inference using these latent variables that act as substitutes for the multi-cause unobserved confounders. We provide a theoretical analysis for obtaining unbiased causal effects of time-varying exposures using the Time Series Deconfounder. Using both simulated and real data we show the effectiveness of our method in deconfounding the estimation of treatment responses over time.
翻译:对治疗效果的估计是医学上普遍存在的一个问题。从纵向观察数据中估算治疗效果的现有方法假定没有隐藏的混淆者,这种假设在实践中是无法测试的,如果无法检测,则会导致偏颇的估计。在本文中,我们开发了时间序列断裂者,这一方法利用长期分配多种治疗的方法,以便在多原因隐蔽者出现的情况下估算治疗效果。时间序列断裂者使用一个具有多任务输出的新的经常性神经网络结构来建立因素模型,并推断出使指定治疗有条件独立的潜在变数;然后,它利用这些潜在变数来进行因果关系推断,这些变数可以替代未观察到的多原因的断裂者。我们提供了理论分析,以便利用时间序列断裂断裂Deconfounder获得时间变化照射的无偏见因果关系效果。我们利用模拟数据和真实数据来显示我们方法在逐渐解析对治疗反应的估计方面的有效性。