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：处理效应估计在因果推断中起着关键作用，具有政策分析与决策制定等多类实际应用。然而，在纵向数据中处理存在隐藏混杂因素时的效应估计仍是一个极具挑战性的问题。近期，大量研究试图忽略隐藏混杂因素存在的可能性，从时间动态观测数据中获得无偏的个体处理效应估计值。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。本文拓展了在动态时间设定下存在隐藏混杂因素时进行去混杂的研究方向。我们利用神经微分方程的最新进展，通过随机受控微分方程与Lipschitz约束卷积操作构建潜因子模型，从而连续整合持续干预与非均匀采样观测的信息。在合成数据集与真实数据集上的实验表明，连续时间方法在处理存在隐藏混杂因素时的效应估计方面具有显著潜力。