Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders. Instrumental variable (IV) is a powerful tool for addressing the latent confounders issue, but the traditional IV technique cannot deal with latent time-dependent confounders in longitudinal studies. In this work, we propose a novel Time-dependent Instrumental Factor Model (TIFM) for time-varying causal effect estimation from data with latent time-dependent confounders. At each time-step, the proposed TIFM method employs the Recurrent Neural Network (RNN) architecture to infer latent IV, and then uses the inferred latent IV factor for addressing the confounding bias caused by the latent time-dependent confounders. We provide a theoretical analysis for the proposed TIFM method regarding causal effect estimation in longitudinal data. Extensive evaluation with synthetic datasets demonstrates the effectiveness of TIFM in addressing causal effect estimation over time. We further apply TIFM to a climate dataset to showcase the potential of the proposed method in tackling real-world problems.

翻译：从纵向观察性数据进行因果推断是一个具有挑战性的问题，原因在于难以正确识别时变混杂因素，尤其是存在潜在时变混杂因素时。工具变量是处理潜在混杂因素问题的有力工具，但传统工具变量技术无法应对纵向研究中潜在的时变混杂因素。本文提出了一种新颖的时变工具因子模型，用于从含有潜在时变混杂因素的数据中估计时变因果效应。在每个时间步，所提出的TIFM方法采用循环神经网络架构推断潜在工具变量，并利用推断出的潜在工具因子来解决由潜在时变混杂因素引起的混杂偏差。我们针对纵向数据中的因果效应估计问题，对所提出的TIFM方法进行了理论分析。基于合成数据集的广泛评估表明，TIFM在随时间进行因果效应估计方面具有有效性。我们进一步将TIFM应用于气候数据集，展示了所提方法处理实际问题的潜力。