建模时间序列中未知干扰因素的因果关系，提升复杂环境下的因果推断准确率。
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
- 引入时序潜在变量建模未知外部干扰对观测变量的影响。
- 通过变分推断融合先验知识，提升参数学习的稳定性和准确性。
- 适用于存在未知干扰且可提供领域知识的因果发现场景。
从观测数据中推断因果关系是一项重要任务，但在受多种外部干扰影响时变得极具挑战性。这些干扰通常表现为外部因素对观测变量的额外作用，而这些外部因素往往未知，因此我们引入潜在变量来表示影响观测数据的未观测因素。为此，提出一种新的时序潜在变量结构因果模型，通过因果强度与邻接系数捕捉变量间的因果关系。考虑到专家知识在特定场景下可提供关于未知干扰的信息，我们开发了一种基于变分推断的方法，将先验知识融入参数学习过程，以指导模型估计。实验结果表明，所提方法在稳定性与准确性方面表现优异。
原文摘要 · Abstract (English)
Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unknown, we introduce latent variables to represent these unobserved factors that affect the observed data. Specifically, to capture the causal strength and adjacency information, we propose a new temporal latent variable structural causal model, incorporating causal strength and adjacency coefficients that represent the causal relationships between variables. Considering that expert knowledge can provide information about unknown interferences in certain scenarios, we develop a method that facilitates the incorporation of prior knowledge into parameter learning based on Variational Inference, to guide the model estimation. Experimental results demonstrate the stability and accuracy of our proposed method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。