Using observational data to estimate the effect of a treatment is a powerful tool for decision-making when randomized experiments are infeasible or costly. However, observational data often yields biased estimates of treatment effects, since treatment assignment can be confounded by unobserved variables. A remedy is offered by deconfounding methods that adjust for such unobserved confounders. In this paper, we develop the Sequential Deconfounder, a method that enables estimating individualized treatment effects over time in presence of unobserved confounders. This is the first deconfounding method that can be used in a general sequential setting (i.e., with one or more treatments assigned at each timestep). The Sequential Deconfounder uses a novel Gaussian process latent variable model to infer substitutes for the unobserved confounders, which are then used in conjunction with an outcome model to estimate treatment effects over time. We prove that using our method yields unbiased estimates of individualized treatment responses over time. Using simulated and real medical data, we demonstrate the efficacy of our method in deconfounding the estimation of treatment responses over time.
翻译:在随机实验不可行或费用昂贵的情况下,利用观察数据来估计治疗的效果是决策的有力工具;然而,观察数据往往产生对治疗效果的偏差估计,因为治疗任务可能由未观察的变量混在一起。一种补救措施是,通过分解方法,为未观察的混淆者进行调整。在本文中,我们开发了序列断裂器,这种方法能够在没有观察到的混淆者在场的情况下估计个人治疗的长期影响。这是在一般顺序环境下可以使用的第一个分解方法(即每步指派一种或多种治疗方法)。序列断裂器使用新的高斯过程潜在变异模型来推断未观察的混淆者替代物,然后与结果模型一起使用该模型来估计一段时间的治疗效果。我们证明,使用我们的方法可以得出对个别治疗反应的不偏差估计。我们使用模拟和实际的医疗数据,展示了我们方法在一段时间内解析治疗反应的功效。