Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making. Nevertheless, estimating treatment effects in the longitudinal setting in the presence of hidden confounders remains an extremely challenging problem. Recently, there is a growing body of work attempting to obtain unbiased ITE estimates from time-dynamic observational data by ignoring the possible existence of hidden confounders. Additionally, many existing works handling hidden confounders are not applicable for continuous-time settings. In this paper, we extend the line of work focusing on deconfounding in the dynamic time setting in the presence of hidden confounders. We leverage recent advancements in neural differential equations to build a latent factor model using a stochastic controlled differential equation and Lipschitz constrained convolutional operation in order to continuously incorporate information about ongoing interventions and irregularly sampled observations. Experiments on both synthetic and real-world datasets highlight the promise of continuous time methods for estimating treatment effects in the presence of hidden confounders.

翻译：治疗效果估计在因果推断中发挥着关键作用，在政策分析和决策制定等众多现实场景中具有重要应用。然而，在纵向数据中存在隐藏混杂因素的情况下进行治疗效果估计仍是一个极具挑战性的问题。近期，越来越多研究尝试通过忽略可能存在的隐藏混杂因素，从时间动态观测数据中获取无偏的个体治疗效果估计值。此外，许多现有处理隐藏混杂因素的方法并不适用于连续时间设定。本文延续了动态时间设定下存在隐藏混杂因素时的去混杂研究方向，利用神经微分方程的最新进展，采用随机受控微分方程和Lipschitz约束卷积操作构建潜在因子模型，从而持续整合正在进行的干预信息与不规则采样的观测数据。在合成数据集和真实数据集上的实验结果表明，连续时间方法在存在隐藏混杂因素时进行治疗效果估计具有显著优势。