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：估计治疗效果在因果推断中起着关键作用,因为有许多实际应用,如政策分析和决策等。然而,在隐蔽的困惑者在场的情况下估计纵向环境中的治疗效果仍是一个极具挑战性的问题。最近,越来越多的工作试图通过忽略隐蔽的困惑者可能存在,从时间动态观测数据中获得公正的 ITE估计值。此外,许多现有的处理隐蔽混淆者的工程不适用于连续时间设置。在本文中,我们扩大了侧重于在隐蔽的共融者在场的情况下动态时间设置中分解的工作范围。我们利用神经差异方程式最近的进展,利用静态控制差异方程式和利普施茨限制的革命运作来建立一个潜在要素模型,以便不断纳入关于持续干预和不定期抽样观察的信息。对合成和真实世界数据集的实验突出表明,在隐藏的共融者在场的情况下,持续使用时间方法估计治疗效果的前景。